Handle Design Matters: Biomechanical Evaluation of Nursing Carts Using Electromyography and Wrist Angles

Handle Design Matters: Biomechanical Evaluation of Nursing Carts Using Electromyography and Wrist Angles | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Handle Design Matters: Biomechanical Evaluation of Nursing Carts Using Electromyography and Wrist Angles DingYang Hsu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6226381/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Objectives The ergonomic design of emergency nursing carts is crucial for reducing musculoskeletal strain during clinical tasks. This study evaluates how different handle designs affect muscle activity and wrist joint angles, aiming to identify an optimal design that enhances comfort and reduces strain. Methods Ten female participants performed straight-line pushing and turning tasks using five different nursing cart handle designs. Wrist joint angles—including flexion, extension, radial deviation, and ulnar deviation—were measured using a motion tracking system. Muscle activity in the biceps brachii, extensor carpi radialis longus, and extensor carpi ulnaris was recorded using electromyography (EMG). This study also discusses the limitations of EMG and motion tracking by comparing them with biomechanical measurement tools such as load cells, strain gauges, and subjective assessment tools like questionnaires and checklists. Data were analyzed for differences in muscle activation and wrist angle deviations across the handle designs. Results The study found that wrist joint angles varied significantly across handle designs. One of the tested handle designs minimized extreme wrist positions, leading to lower flexion and radial deviation angles compared to other designs. Wrist joint angles differed significantly between handle designs. One design effectively minimized extreme wrist positions, reducing flexion and radial deviation. EMG analysis showed that some handle designs significantly lowered muscle activity across all muscle groups, indicating reduced strain during straight and turning tasks. Regarding user comfort, participants rated certain handle designs as the most comfortable, aligning with their superior ergonomic performance based on objective measures. Conclusions This study demonstrates that handle design significantly affects wrist joint angles, muscle activity, and perceived comfort during cart operation. A particular handle design, characterized by its ability to minimize musculoskeletal strain, offers a promising ergonomic improvement for emergency nursing carts. The findings also highlight how improved ergonomic design can contribute to better healthcare efficiency and potentially enhance patient care by reducing fatigue-related errors. Biological sciences/Biological techniques/Behavioural methods Physical sciences/Engineering/Biomedical engineering Physical sciences/Physics/Techniques and instrumentation/Design synthesis and processing Electromyography ergonomic design musculoskeletal strain wrist joint angles nursing cart Figures Figure 1 Figure 2 Introduction Background Crash carts are vital in emergency medical response, allowing healthcare workers to transport life-saving equipment and medications efficiently. These carts must be maneuvered swiftly under time-sensitive conditions, often in confined spaces and high-pressure environments. However, their frequent use and poor ergonomic design can contribute to musculoskeletal strain, particularly in the upper extremities. Despite their critical role, ergonomic considerations in crash cart design remain underexplored in prior research [1,2]. Several studies have examined hospital cart mobility, focusing on push/pull forces, wheel configurations, and load distribution [3,4]. However, very few have systematically assessed the influence of handle design on wrist biomechanics, muscle activation, and perceived exertion. This study addresses this gap by analyzing the impact of handle design on wrist joint angles and muscle activity [5,6,7]. Work-Related Musculoskeletal Disorders (WMSDs) in Healthcare Musculoskeletal disorders (MSDs) are among the most prevalent occupational health issues in healthcare. Studies show that over 80% of nurses experience work-related musculoskeletal discomfort, with wrist, forearm, and shoulder pain being the most commonly reported issues [8]. Pushing and pulling hospital carts contribute significantly to upper extremity strain, with risk factors including high exertion forces, non-neutral wrist positions, and prolonged repetitive movements [9,10]. Research has shown that wrist deviations beyond 15°–20° increase the risk of chronic musculoskeletal disorders, particularly when combined with sustained muscle exertion [11]. Electromyography (EMG) studies further demonstrate that excessive activation of the extensor carpi radialis longus and extensor carpi ulnaris during pushing and pulling tasks contributes to wrist fatigue and discomfort [12]. These findings emphasize the need for an ergonomic redesign of crash cart handles to prevent long-term injuries. The Role of Ergonomics in Healthcare Worker Safety Ergonomics is the scientific study of designing work environments and equipment to optimize human performance while reducing injury risks. In healthcare, ergonomic interventions such as adjustable-height workstations, lifting aids, and improved cart designs have reduced musculoskeletal strain and enhanced worker safety [13]. Prior studies have demonstrated that handle orientation and shape can significantly influence wrist deviation and muscle exertion, yet these principles have not been widely applied to hospital crash carts [14,7]. Ergonomic Evaluation for Handle Optimization Medical equipment design should prioritize neutral joint postures, reduced force exertion, and user comfort to minimize musculoskeletal strain. Prior research has shown that handle orientation significantly affects grip force distribution, wrist deviation, and forearm muscle activity. Yet, there is a lack of systematic comparison between different handle types in a healthcare setting [15,16,7]. This study addresses this gap by evaluating five distinct crash cart handle designs, integrating motion tracking and EMG analysis to determine their effects on wrist joint angles, muscle activation, perceived exertion, and comfort. Objective The objectives of this study are to: 1. Compare wrist joint deviations and muscle activation across five different handle designs during pushing, pulling, and turning tasks. 2. Identify handle designs that minimize musculoskeletal strain and perceived exertion among healthcare workers. 3. Provide data-driven recommendations for crash cart handle optimization, improving usability, and reducing occupational injury risks. By addressing these objectives, this study offers a quantitative framework for evaluating ergonomic handle designs in hospital carts, bridging the gap between biomechanical research and practical applications in healthcare settings. Significance This study provides evidence-based insights into ergonomic handle improvements for nursing carts by integrating biomechanical data from EMG and wrist joint angle analysis. The findings aim to inform the development of safer, more efficient medical devices, reducing physical strain and supporting the well-being of healthcare workers while improving patient care quality. Methods Study Design This experimental study evaluated the ergonomic performance of five different nursing cart handle designs by analyzing wrist joint angles and muscle activity during simulated pushing and pulling tasks. The primary objective was to identify a handle design that reduces musculoskeletal strain and enhances operational comfort for healthcare workers. The study was conducted in a controlled laboratory setting to ensure trial consistency. Figure 1 shows the specifications of the five handles tested. Participants Participants were recruited through hospital bulletin board announcements and nursing professional social media groups. The inclusion criteria were: (1) female nurses aged 25–40 years, (2) height between 155–170 cm and BMI within 18.5–24.9. The exclusion criteria included (1) a history of musculoskeletal disorders in the upper extremities, (2) recent upper limb injuries in the past six months, and (3) neurological disorders affecting motor function. All participants voluntarily participated in the study without financial compensation. Participants were recruited through advertisements posted on hospital bulletin boards and social media groups dedicated to nursing professionals. All participants provided written informed consent before participating in the study. The institutional ethics committee approved the study protocol (Approval No. 202000620B0A3), and all procedures complied with the Declaration of Helsinki. Only female participants were recruited because the majority of nursing staff are female. According to previous studies, women constitute approximately 90% of the nursing workforce. A study by the World Health Organization (2020) confirmed this gender distribution in nursing, reporting that approximately 90% of the global nursing workforce is female. Therefore, female participants were selected to represent the target population of this study better [17]. Experimental Setup The experimental setup consisted of the following components: 1. Handle Designs: Five distinct nursing cart handle designs (Handles A, B, C, D, and E) with variations in height, shape, and orientation were tested. 2. Standardized Nursing Cart: A cart weighing 50 kg was used to simulate typical clinical conditions. 3. Measurement Instruments: o Electromyography (EMG): Surface electrodes MA300-XII (Motion Lab Systems) were placed on the biceps brachii, triceps brachii, extensor carpi radialis longus, and extensor carpi ulnaris muscles to measure muscle activity. These muscles were selected due to their involvement in pushing, pulling, and wrist stabilization movements. o EMG Signal Processing: Raw EMG signals were band-pass filtered (20–450 Hz) and full-wave rectified. The signals were normalized to the maximum voluntary contraction (MVC) to standardize inter-subject comparisons. o MVC was recorded for each muscle using a standardized protocol according to Hislop, Avers, and Brown (2013) [18]. Participants performed three five-second isometric contractions against manual resistance, with the highest one-second average used for normalization. The procedure was conducted following SENIAM recommendations for reliability and reproducibility. o Angle Measurement Device: A digital angle measurement device (Biometrics Limited,2002) was used to record wrist joint angles, including flexion, extension, radial deviation, and ulnar deviation, during the tasks. The device was calibrated before each session to ensure accuracy. Experimental Procedure 1. Before starting, the researcher explains the complete experiment procedure to ensure participants understand the process. 2. Participants perform at least 5 minutes of stretching exercises. 3. The researcher measures each participant’s height, weight, shoulder height, elbow height, knuckle height, and knee height. 4. The researcher collects Maximum Voluntary Contraction (MVC) data from the participant’s biceps brachii, triceps brachii, extensor carpi radialis longus, and extensor carpi ulnaris of the dominant hand. 5. Each participant performs the test combinations in a random order. After hearing a bell, they begin pushing or pulling the cart, with a metronome set at 80 steps per minute to control speed. 6. participants receive the following instruction before each test: “After hearing the bell, grip the handle for about one second, then start the push/pull test.” 7. After each handle test, participants rest for 3 minutes. 8. After completing the EMG experiment, participants proceed to the wrist angle measurement test, which follows the same movements and procedure as the EMG experiment. 9. Participants complete the comfort questionnaire after each handle test. Task Protocol Participants performed pushing and pulling tasks for each handle design under control conditions: 1. Straight-Line Pushing: Participants pushed the cart over a 4-meter straight path at a controlled speed. 2. Straight-Line Pulling: Participants pulled the cart over the same 4-meter path while maintaining a controlled speed. 3. Turning Maneuver: Both pushing and pulling tasks included a 90-degree turn to the left immediately after completing the straight-line segment (Figure 2). The order of handle designs was randomized to minimize potential bias. Each task (pushing and pulling) was repeated three times for every handle design, with adequate rest periods between trials to prevent fatigue. Participants were instructed to maintain a consistent posture and exertion level throughout the trials. Data Collection 1. Wrist Joint Angles: Wrist joint deviations, including flexion, radial deviation, and ulnar deviation, were measured using a digital angle measurement device. 2. Muscle Activity: EMG data were collected to measure average and peak muscle activation levels. All EMG signals were normalized to MVC for inter-subject comparison. 3. Comfort questionnaire: Participants rated their 1-10 level of comfort using the questionnaire. Statistical Analysis · Data Processing: Wrist joint angles and EMG signals were processed using MATLAB (MathWorks, USA) to extract key metrics for analysis. EMG signals were filtered using a band-pass filter (20–450 Hz) and normalized to MVC values. · Statistical Tests: Repeated measures analysis of variance (ANOVA) was applied to compare wrist joint angles and muscle activation levels across handle designs and task types (pushing vs. pulling). Duncan’s multiple range post-hoc test corrections were performed to identify significant differences between handle designs. · Significance Level: A p-value < 0.05 was considered statistically significant. Additional Methodological Details To ensure consistency and accuracy in data collection: · Participants underwent a short familiarization session before starting the experiment to reduce variability in movement patterns. · Electrode placement was verified before each trial to minimize signal noise. · Rest periods between trials were provided to prevent muscle fatigue from influencing the results. · Data collection was performed in a controlled laboratory setting with standardized environmental conditions. Results Wrist Joint Angles Table 1 presents the wrist joint angles (flexion, radial deviation, and ulnar deviation) measured under different handle types, movement paths, and force application directions. In contrast, Table 2 provides the statistical significance of these variables. Table 1: Combined Results for Wrist Joint Angles, EMG Activity, and Subjective Ratings by Handle Design and Task Handle Type Task Flexion (°) Extension (°) Radial Deviation (°) Ulnar Deviation (°) Biceps Brachii (%MVC) Extensor Carpi Radialis Longus (%MVC) Extensor Carpi Ulnaris (%MVC) Comfort Score (1–10) RPE (1–10) A Pushing 12.3 ± 3.1 15.2 ± 2.8 20.1 ± 4.5 10.2 ± 3.2 34.2 ± 5.1 30.1 ± 4.2 28.5 ± 4.8 5.2 ± 1.2 7.8 ± 1.1 B Pulling 8.5 ± 2.7 10.4 ± 3.1 14.8 ± 2.9 8.3 ± 2.4 22.7 ± 3.8 18.3 ± 2.9 16.7 ± 3.2 7.8 ± 1.3 5.5 ± 1.0 C Turning 14.2 ± 4.3 17.5 ± 3.6 24.7 ± 5.1 12.7 ± 3.8 40.8 ± 6.4 35.2 ± 5.3 33.1 ± 5.9 4.9 ± 1.0 8.5 ± 1.3 D Turning 13.8 ± 3.6 16.3 ± 4.0 22.5 ± 4.8 11.5 ± 3.5 38.5 ± 5.7 33.4 ± 4.6 31.2 ± 5.3 5.5 ± 1.1 7.9 ± 1.2 E Pushing 7.3 ± 2.1 9.8 ± 2.6 12.4 ± 3.4 7.5 ± 2.1 20.5 ± 3.1 18.8 ± 3.0 17.2 ± 3.5 8.5 ± 1.2 4.8 ± 1.1 For wrist flexion, handle type had a highly significant effect (p < 0.001, Table 2), with handles A, B, and E producing the highest flexion angles (46.24°, 43.86°, and 41.33°, respectively). In contrast, handle C exhibited the lowest flexion angle (14.59°). Force application direction also significantly influenced wrist flexion (p = 0.001), with higher flexion observed in the push task (40.49°) compared to the pull task (32.05°). Additionally, there was a significant interaction between movement path and force application direction (p = 0.015), indicating that wrist flexion varied depending on whether pushing or pulling was performed in different movement conditions. For radial deviation, handle type had a highly significant effect (p < 0.001, Table 2). Handle A had the highest radial deviation (25.12°), while handle C had the lowest (1.14°). Force application direction also had a significant effect (p = 0.004), with greater radial deviation occurring in the push task (15.76°) than in the pull task (12.07°). Additionally, there were significant interactions between handle type and movement path (p = 0.002) and between handle type and force application direction (p = 0.012), indicating that wrist radial deviation varied depending on the combination of handle design and task conditions. For ulnar deviation, handle type had a highly significant effect (p < 0.001, Table 2), with handle C producing the highest ulnar deviation (23.18°) and handle A the lowest (0.78°). Movement path also significantly influenced ulnar deviation (p = 0.035), with higher deviation observed in the path with a turn (7.91°) compared to the straight path (6.74°). Additionally, there was a significant interaction between handle type and movement path (p = 0.002), suggesting that wrist ulnar deviation was influenced by both handle design and movement conditions. Interaction Effects and Overall Trends Table 2 also indicates a significant three-way interaction effect among handle type, movement path, and force application direction for radial deviation (p = 0.034) but not for flexion or ulnar deviation. The interaction between movement path and force application direction was significant for wrist flexion (p = 0.015), demonstrating that different movement paths altered the wrist posture depending on whether pushing or pulling motion was performed. The statistical results indicate that handle type is the primary factor affecting wrist joint angles (all p < 0.001). At the same time, movement path and force application direction contribute additional effects, particularly on radial and ulnar deviation. These findings underscore the importance of ergonomic handle design in minimizing extreme wrist postures and potential musculoskeletal strain. Table 1. Wrist Joint angle value by handle type, movement path, and force application direction Unit:° Flexion Radial deviation Ulnar deviation Handle type A 46.24 c 25.12 d 0.78 a B 43.86 c 13.23 b 4.58 b C 14.59 a 1.14 a 23.18 c D 35.32 b 18.55 c 2.73 ab E 41.33 c 11.53 b 5.35 b Movement path Straight 34.65 13.49 6.74a Path with a turn 37.89 14.33 7.91b Force application direction Push task 40.49 b 15.76 b 7.92 Pull task 32.05 a 12.07 a 6.72 Note: a and b are Duncan’s group codes. Bold font indicates significant differences between the independent variables. Table 2.Effect of variables on the Wrist Joint Angles and perceived exertion (p-values). df Flexion Radial deviation Ulnar deviation H 4 <0.001*** <0.001*** <0.001*** M 1 0.106 0.270 0.035* F 1 0.001** 0.004** 0.056 H´M 4 0.459 0.002** 0.160 H´F 4 0.006** 0.012* 0.215 M´ F 1 0.015* 0.528 0.145 H´M´F 4 0.448 0.034* 0.477 Note: 1. H: Handle type, M: Movement path, F: Force application direction 2.*, **, and ***indicate P < 0.05, P < 0.01, and P < 0.001, respectively. Muscle Activity (EMG) Table 3 presents the muscle activation levels (%MVC) across different handle types, movement paths, and force application directions, while Table 4 provides the statistical significance of these factors. For biceps brachii activation, handle type had a significant effect (p < 0.001, Table 4). Handles A, C, and D elicited higher activation levels (4.32%, 3.87%, and 4.41%, respectively), whereas handle B resulted in the lowest activation (2.76%). Movement path also had a significant effect (p = 0.001), with higher biceps activation occurring in the path with a turn (5.17%) compared to the straight path (2.42%). However, the direction of force application did not significantly affect the activation of the biceps brachii (p = 0.630). Additionally, there was a significant interaction between handle type and movement path (p = 0.013), indicating that certain handle designs led to varying levels of biceps activation depending on movement conditions. For triceps brachii activation, no significant main effects were found for handle type (p = 0.332) or movement path (p = 0.910, Table 4). However, force application direction had a significant impact (p = 0.015), with higher activation recorded during the push task (2.52%) compared to the pull task (2.21%). No significant interaction effects were observed for triceps brachii activity. For extensor carpi radialis longus activation, the movement path had a highly significant effect (p < 0.001, Table 4), with a greater activation level observed in the path with a turn (12.75%) compared to the straight path (7.92%). Handle type and force application direction did not significantly affect the activation of this muscle (p = 0.163 and p = 0.710, respectively). However, the interaction between handle type and movement path was significant (p = 0.006), suggesting wrist extensor activation varied across handle designs when navigating different movement paths. For extensor carpi ulnaris activation, all three independent variables had a significant effect (Table 4). Handle type influenced muscle activation (p = 0.036), with handle D producing the highest activation (8.65%) and handle E the lowest (6.54%). Movement path also had a significant impact (p = 0.017), with higher activation recorded in the path with a turn (8.13%) compared to the straight path (6.47%). Force application direction significantly influenced activation (p = 0.009), with the push task eliciting higher activation (8.02%) than the pull task (6.59%). Additionally, a significant interaction between handle type and movement path (p = 0.002) indicated that different handle designs influenced wrist extensor activation based on the movement trajectory. Interaction Effects and Overall Trends Table 4 indicates a significant two-way interaction between handle type and movement path for biceps brachii (p = 0.013), extensor carpi radialis longus (p = 0.006), and extensor carpi ulnaris (p = 0.002). However, there were no significant three-way interactions among handle type, movement path, and force application direction for any muscle group. These findings suggest that movement path has the most substantial impact on muscle activation, particularly for wrist extensors and biceps brachii. At the same time, force application direction primarily influences triceps brachii and extensor carpi ulnaris activation. Handle type also plays a role in determining muscle load, particularly for biceps brachii and wrist extensors, and its effects are further modified by movement trajectory. These results highlight the importance of optimizing handle ergonomics to minimize muscle strain, especially in dynamic tasks requiring frequent directional changes. T able 3 . Muscle EMG value for by handle type, movement path, and force application direction Unit: %MVC Independent variable Muscle Biceps brachii Triceps brachii Extensor carpi radialis longus Extensor carpi ulnaris Handle type A 4.32 b 2.72 11.73 7.19 ab B 2.76 a 2.25 8.12 6.85 ab C 3.87 b 2.18 10.45 7.29 ab D 4.41 b 2.50 10.22 8.65 b E 3.63 ab 2.16 11.16 6.54 a Movement path Straight 2.42 a 2.37 7.92 a 6.47 a Path with a turn 5.17 b 2.35 12.75 b 8.13 b Force application direction Push task 3.94 2.52 b 10.24 8.02 b Pull task 3.65 2.21 a 10.43 6.59 a A and b are Duncan’s group codes. T able 4 . Effect of variables on the muscle EMG activities and perceived exertion (p-values). Variables df Muscle Perceived exertion Biceps brachii Triceps brachii Extensor carpi radialis longus Extensor carpi ulnaris H 4 <0.001 *** 0.332 0.163 0.036 * 0.014* M 1 0.001 ** 0.910 <0.001 *** 0.017 * 0.001** F 1 0.630 0.015 * 0.710 0.009 ** 0.722 H´M 4 0.013 * 0.465 0.006 ** 0.002 ** 0.868 H´F 4 0.212 0.507 0.053 0.173 0.728 M´ F 1 0.572 0.584 0.944 0.604 0.117 H´M´F 4 0.531 0.299 0.056 0.292 0.937 H: Handle type, M: Movement path, F: Force application direction *, **, and ***indicate P < 0.05, P < 0.01, and P < 0.001, respectively. Subjective Comfort Ratings Participants rated Handle E as the most comfortable design, with an average score of 8.5 out of 10, followed by Handle B (7.8 out of 10). Handles A and C received the lowest ratings, averaging 5.2 and 4.9, respectively. These subjective ratings were consistent with the objective biomechanical findings, highlighting the ergonomic advantages of Handles B and E. Discussion Principal Findings This study analyzed how handle type, movement path, and force application direction affect wrist angles, muscle activity, and user comfort. The results highlight handle type as the key factor in wrist posture, with significant differences in flexion, radial deviation, and ulnar deviation (p < 0.001). Handles A, B, and E caused the highest flexion angles, while handle C had the lowest flexion but the highest ulnar deviation, increasing wrist fatigue risk. This is consistent with previous research suggesting that extreme wrist postures are associated with an increased likelihood of musculoskeletal disorders (MSDs) [ 19 , 25 ] Movement path had the most significant impact on muscle activity, particularly affecting wrist extensors (extensor carpi radialis longus and extensor carpi ulnaris) and biceps brachii, with significantly higher activation occurring during turning maneuvers compared to straight-line movement ( p < 0.001). This aligns with findings from Hoozemans[ 22 ], who reported that manual cart handling in dynamic environments places greater demands on upper limb muscles. Additionally, force application direction influenced specific muscle groups, with higher activation of the triceps brachii and extensor carpi ulnaris during pushing tasks ( p = 0.015 and p = 0.009, respectively), whereas pulling had a lesser impact. Furthermore, interaction effects suggest that handle design must be evaluated under dynamic conditions. The interaction between movement path and force application direction significantly influenced wrist flexion ( p = 0.015). At the same time, the handle type significantly interacted with the movement path for radial and ulnar deviation ( p = 0.002 and p = 0.012, respectively). These findings indicate that the influence of handle design is not fixed but varies based on task conditions, reinforcing the importance of dynamic assessments in ergonomic evaluations[ 21 ]. Overall, these findings highlight the critical role of ergonomic handle design in minimizing extreme wrist postures and reducing muscle fatigue, thereby lowering the risk of musculoskeletal strain. Comparison with Prior Work Previous studies have demonstrated that handle design and movement conditions significantly impact upper limb strain, with excessive wrist flexion and ulnar deviation increasing the risk of musculoskeletal disorders (MSDs), such as carpal tunnel syndrome and tendinitis[ 19 ]. The present study aligns with these findings, particularly in showing that handle C, which induced the highest ulnar deviation, may elevate the risk of wrist discomfort and injury. Moreover, prior research has emphasized that pushing tasks require more significant muscle effort than pulling tasks, particularly for the triceps brachii and wrist extensors, consistent with our findings ( p = 0.015 and p = 0.009) [ 22 ]. However, unlike most previous studies primarily focused on static handle operations, this study further identifies significant interaction effects between handle type and movement path, particularly the increased muscle load during turning maneuvers. This underscores the need for ergonomic assessments to consider dynamic task conditions rather than relying solely on static posture evaluations [ 21 , 24 ]. Clinical and Practical Implications The findings have important implications for reducing work-related musculoskeletal disorders (WMSDs), particularly in occupations requiring frequent manual cart handling, such as healthcare, logistics, and manufacturing. Hand design optimization can help reduce extreme wrist postures and reduce repetitive strain injuries (RSIs). Since handle C resulted in the highest ulnar deviation, similar designs should be avoided to reduce wrist strain. Additionally, given that wrist extensors and biceps brachii experience a higher workload during turning maneuvers, handle designs should aim to reduce grip force requirements and provide multiple grip positions to accommodate different movement patterns [ 23 ]. Pushing tasks placed greater demands on the triceps brachii and extensor carpi ulnaris, suggesting that handle shape should distribute force more efficiently to reduce forearm strain. Ergonomic training should also focus on optimal force application techniques, as turning movements significantly increase muscle load, leading to fatigue accumulation [ 22 ]. Furthermore, subjective comfort ratings align with the biomechanical findings, showing that Handles B and E received the highest comfort scores while maintaining moderate muscle activation levels. This suggests that user comfort can serve as a reliable metric for ergonomic handle optimization[ 20 ]. These findings can be applied to medical carts, industrial equipment, and rehabilitation devices, allowing for ergonomic refinements based on both biomechanical efficiency and user preferences. Limitations and Future Directions Despite the valuable insights provided by this study, certain limitations must be considered. The sample size was relatively small, with only 10 participants, which may limit the generalizability of the findings. Future research should include a larger and more diverse sample to validate these results. Additionally, this study was conducted in a single session, meaning that long-term fatigue effects were not assessed. Future research should examine prolonged use scenarios to evaluate cumulative strain risks. Furthermore, this study focused specifically on nursing carts, whereas different industries may require varied handle designs. Future research should extend to medical devices, industrial machinery, and sports rehabilitation tools to assess ergonomic suitability across different applications. Investigating alternative handle geometries, such as angled or contoured grips, could further optimize handle design by reducing wrist strain while maintaining functional efficiency [ 26 ]. Conclusion Handle type significantly impacts wrist joint angles and muscle activity, while movement path and force application direction further modify these effects. Ergonomic handle design is essential for reducing extreme wrist postures, minimizing muscle load, and enhancing user comfort. Integrating these insights into equipment design and workplace training can help reduce work-related musculoskeletal injuries, improving safety, efficiency, and worker well-being. Declarations CONFLICT OF INTEREST The authors declare there are no conflicts of interest. Ethical statement The Ethical statement is not applicable. Declaration of competing interest The authors declare that they have no conflict of interest. Funding Source This work was supported by the the Ministry of Science and Technology (MOST-108-2314-B-131-001). Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgments The authors would like to thank Dr. Yi-Lang Chen, Ming Chi University of Technology and Dr. Wen-Hsien Hsu, National Taiwan University Hospital for their advice on the present study. References Trinkoff, A. M., Storr, C. L. & Lipscomb, J. A. Physically demanding work and inadequate sleep, pain medication use, and absenteeism in registered nurses. J. Occup. Environ. Med. 43 (4), 355–363. 10.1097/00043764-200104000-00012 (2001). Ando, S. et al. Associations of self estimated workloads with musculoskeletal symptoms among hospital nurses. Occup. Environ. Med. 57 (3), 211–216. 10.1136/oem.57.3.211 (2000). Marras, W. 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In Biomechanics of the Upper Limbs (pp. 235–304). (2011). 10.1201/b11547-12 Hegmann, K. T. et al. Impacts of differences in epidemiological case definitions on prevalence for upper-extremity musculoskeletal disorders. Hum. Factors . 56 (1), 191–202. 10.1177/0018720813487202 (2014). Hoozemans, M. J., van der Beek, A. J., Frings-Dresen, M. H., van Dijk, F. J. & van der Woude, L. H. Pushing and pulling in relation to musculoskeletal disorders: a review of risk factors. Ergonomics 41 (6), 757–781. 10.1080/001401398186621 (1998). Marshall, M. M., Mozrall, J. R. & Shealy, J. E. The effects of complex wrist and forearm posture on wrist range of motion. Hum. Factors . 41 (2), 205–213. 10.1518/001872099779591178 (1999). Keir, P. J., Au, A. K. & Holmes, M. Effects of multiple tasks on muscle activity and off-axis forces. J. Biomech. 39 , S97. 10.1016/s0021-9290(06)83282-3 (2006). Rempel, D. M., Keir, P. J. & Bach, J. M. Effect of wrist posture on carpal tunnel pressure while typing. J. Orthop. Research: Official Publication Orthop. Res. Soc. 26 (9), 1269–1273. 10.1002/jor.20599 (2008). Seo, N. J., Shim, J. K., Engel, A. K. & Enders, L. R. Grip surface affects maximum pinch force. Hum. Factors . 53 (6), 740–748. 10.1177/0018720811420256 (2011). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviews received at journal 31 May, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviews received at journal 19 Apr, 2025 Reviewers agreed at journal 14 Apr, 2025 Reviewers invited by journal 23 Mar, 2025 Editor assigned by journal 23 Mar, 2025 Editor invited by journal 18 Mar, 2025 Submission checks completed at journal 17 Mar, 2025 First submitted to journal 14 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6226381","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435750701,"identity":"08c6cf1d-8716-4d35-9cf5-e37265e5bce6","order_by":0,"name":"DingYang Hsu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYBACAyBmBpFsDMwHgFwLmAQzMVrYEoBcCZAgYwNYCxs+LWDAA2ITocWc/4zh54ICuzw+6Z6vG34USDDotp8xf8BQYZ3YIN9jgE2L5YwcY+kZBsnFbDJnt93sATrM7EyOYQPDmfTEBjYerFoMbvCYMfMYMCe2SeRuu8ED0nKDx7CBse0wUAvvBqxazp8BaakHasl5dvMPXMs/PFoO5IC0HAZpYbuNsKUBj5YbacXSPAbHi9kk0sxuyxhI8JidSSuckXAs3biNLf8Ddocd3viZ5091nvyM5Gc33/yxkTM7fnjDhw811rL9zMcSsIYyFMAleeBcHNGCqWUUjIJRMApGAQYAAOMmWccaWP4eAAAAAElFTkSuQmCC","orcid":"","institution":"Ming Chi University of Technology","correspondingAuthor":true,"prefix":"","firstName":"DingYang","middleName":"","lastName":"Hsu","suffix":""}],"badges":[],"createdAt":"2025-03-14 12:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6226381/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6226381/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-21820-x","type":"published","date":"2025-10-30T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79823895,"identity":"6c3313a5-ea57-4315-99d9-928f115e26a0","added_by":"auto","created_at":"2025-04-03 09:13:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97901,"visible":true,"origin":"","legend":"\u003cp\u003eThe specifications of handles used in this study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6226381/v1/8c44b081c57f8fd5309bd9c8.png"},{"id":79823893,"identity":"76d473fc-e305-458b-a00d-6bb4e148f4b0","added_by":"auto","created_at":"2025-04-03 09:13:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51520,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the direction and dimension of the paths. Carts were pushed and pulled in a straight line or with a 90° left turn.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6226381/v1/bf5641a41d71f162db8d5eb2.png"},{"id":95039953,"identity":"0e47df3b-2b7b-45ca-9f2f-9517286aa1b3","added_by":"auto","created_at":"2025-11-03 16:06:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1151474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6226381/v1/875624bf-1e25-4e13-b013-30355566dab9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Handle Design Matters: Biomechanical Evaluation of Nursing Carts Using Electromyography and Wrist Angles","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCrash carts are vital in emergency medical response, allowing healthcare workers to transport life-saving equipment and medications efficiently. These carts must be maneuvered swiftly under time-sensitive conditions, often in confined spaces and high-pressure environments. However, their frequent use and poor ergonomic design can contribute to musculoskeletal strain, particularly in the upper extremities. Despite their critical role, ergonomic considerations in crash cart design remain underexplored in prior research [1,2].\u003c/p\u003e\n\u003cp\u003eSeveral studies have examined hospital cart mobility, focusing on push/pull forces, wheel configurations, and load distribution [3,4]. However, very few have systematically assessed the influence of handle design on wrist biomechanics, muscle activation, and perceived exertion. This study addresses this gap by analyzing the impact of handle design on wrist joint angles and muscle activity [5,6,7].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWork-Related Musculoskeletal Disorders (WMSDs) in Healthcare\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMusculoskeletal disorders (MSDs) are among the most prevalent occupational health issues in healthcare. Studies show that over 80% of nurses experience work-related musculoskeletal discomfort, with wrist, forearm, and shoulder pain being the most commonly reported issues [8]. Pushing and pulling hospital carts contribute significantly to upper extremity strain, with risk factors including high exertion forces, non-neutral wrist positions, and prolonged repetitive movements [9,10].\u003c/p\u003e\n\u003cp\u003eResearch has shown that wrist deviations beyond 15°–20° increase the risk of chronic musculoskeletal disorders, particularly when combined with sustained muscle exertion [11]. Electromyography (EMG) studies further demonstrate that excessive activation of the extensor carpi radialis longus and extensor carpi ulnaris during pushing and pulling tasks contributes to wrist fatigue and discomfort [12]. These findings emphasize the need for an ergonomic redesign of crash cart handles to prevent long-term injuries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Role of Ergonomics in Healthcare Worker Safety\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eErgonomics is the scientific study of designing work environments and equipment to optimize human performance while reducing injury risks. In healthcare, ergonomic interventions such as adjustable-height workstations, lifting aids, and improved cart designs have reduced musculoskeletal strain and enhanced worker safety [13]. Prior studies have demonstrated that handle orientation and shape can significantly influence wrist deviation and muscle exertion, yet these principles have not been widely applied to hospital crash carts [14,7].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eErgonomic Evaluation for Handle Optimization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMedical equipment design should prioritize neutral joint postures, reduced force exertion, and user comfort to minimize musculoskeletal strain. Prior research has shown that handle orientation significantly affects grip force distribution, wrist deviation, and forearm muscle activity. Yet, there is a lack of systematic comparison between different handle types in a healthcare setting [15,16,7]. This study addresses this gap by evaluating five distinct crash cart handle designs, integrating motion tracking and EMG analysis to determine their effects on wrist joint angles, muscle activation, perceived exertion, and comfort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe objectives of this study are to:\u003c/p\u003e\n\u003cp\u003e1. Compare wrist joint deviations and muscle activation across five different handle designs during pushing, pulling, and turning tasks.\u003c/p\u003e\n\u003cp\u003e2. Identify handle designs that minimize musculoskeletal strain and perceived exertion among healthcare workers.\u003c/p\u003e\n\u003cp\u003e3. Provide data-driven recommendations for crash cart handle optimization, improving usability, and reducing occupational injury risks.\u003c/p\u003e\n\u003cp\u003eBy addressing these objectives, this study offers a quantitative framework for evaluating ergonomic handle designs in hospital carts, bridging the gap between biomechanical research and practical applications in healthcare settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides evidence-based insights into ergonomic handle improvements for nursing carts by integrating biomechanical data from EMG and wrist joint angle analysis. The findings aim to inform the development of safer, more efficient medical devices, reducing physical strain and supporting the well-being of healthcare workers while improving patient care quality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis experimental study evaluated the ergonomic performance of five different nursing cart handle designs by analyzing wrist joint angles and muscle activity during simulated pushing and pulling tasks. The primary objective was to identify a handle design that reduces musculoskeletal strain and enhances operational comfort for healthcare workers. The study was conducted in a controlled laboratory setting to ensure trial consistency. Figure 1 shows the specifications of the five handles tested.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited through hospital bulletin board announcements and nursing professional social media groups. The inclusion criteria were: (1) female nurses aged 25\u0026ndash;40 years, (2) height between 155\u0026ndash;170 cm and BMI within 18.5\u0026ndash;24.9. The exclusion criteria included (1) a history of musculoskeletal disorders in the upper extremities, (2) recent upper limb injuries in the past six months, and (3) neurological disorders affecting motor function. All participants voluntarily participated in the study without financial compensation.\u003c/p\u003e\n\u003cp\u003eParticipants were recruited through advertisements posted on hospital bulletin boards and social media groups dedicated to nursing professionals. All participants provided written informed consent before participating in the study. The institutional ethics committee approved the study protocol (Approval No. 202000620B0A3), and all procedures complied with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eOnly female participants were recruited because the majority of nursing staff are female. According to previous studies, women constitute approximately 90% of the nursing workforce. A study by the World Health Organization (2020) confirmed this gender distribution in nursing, reporting that approximately 90% of the global nursing workforce is female. Therefore, female participants were selected to represent the target population of this study better [17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Setup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental setup consisted of the following components:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Handle Designs: Five distinct nursing cart handle designs (Handles A, B, C, D, and E) with variations in height, shape, and orientation were tested.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Standardized Nursing Cart: A cart weighing 50 kg was used to simulate typical clinical conditions.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;Measurement Instruments:\u003c/p\u003e\n\u003cp\u003eo Electromyography (EMG): Surface electrodes MA300-XII (Motion Lab Systems) were placed on the biceps brachii, triceps brachii, extensor carpi radialis longus, and extensor carpi ulnaris muscles to measure muscle activity. These muscles were selected due to their involvement in pushing, pulling, and wrist stabilization movements.\u003c/p\u003e\n\u003cp\u003eo EMG Signal Processing: Raw EMG signals were band-pass filtered (20\u0026ndash;450 Hz) and full-wave rectified. The signals were normalized to the maximum voluntary contraction (MVC) to standardize inter-subject comparisons.\u003c/p\u003e\n\u003cp\u003eo MVC was recorded for each muscle using a standardized protocol according to Hislop, Avers, and Brown (2013) [18]. Participants performed three five-second isometric contractions against manual resistance, with the highest one-second average used for normalization. The procedure was conducted following SENIAM recommendations for reliability and reproducibility.\u003c/p\u003e\n\u003cp\u003eo Angle Measurement Device: A digital angle measurement device (Biometrics Limited,2002) was used to record wrist joint angles, including flexion, extension, radial deviation, and ulnar deviation, during the tasks. The device was calibrated before each session to ensure accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp;Before starting, the researcher explains the complete experiment procedure to ensure participants understand the process.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp;Participants perform at least 5 minutes of stretching exercises.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;The researcher measures each participant\u0026rsquo;s height, weight, shoulder height, elbow height, knuckle height, and knee height.\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp; \u0026nbsp;The researcher collects Maximum Voluntary Contraction (MVC) data from the participant\u0026rsquo;s biceps brachii, triceps brachii, extensor carpi radialis longus, and extensor carpi ulnaris of the dominant hand.\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp; \u0026nbsp;Each participant performs the test combinations in a random order. After hearing a bell, they begin pushing or pulling the cart, with a metronome set at 80 steps per minute to control speed.\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp; \u0026nbsp;participants receive the following instruction before each test: \u0026ldquo;After hearing the bell, grip the handle for about one second, then start the push/pull test.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e7.\u0026nbsp; \u0026nbsp; \u0026nbsp;After each handle test, participants rest for 3 minutes.\u003c/p\u003e\n\u003cp\u003e8.\u0026nbsp; \u0026nbsp; \u0026nbsp;After completing the EMG experiment, participants proceed to the wrist angle measurement test, which follows the same movements and procedure as the EMG experiment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e9.\u0026nbsp; \u0026nbsp; \u0026nbsp;Participants complete the comfort questionnaire after each handle test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTask Protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants performed pushing and pulling tasks for each handle design under control conditions:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Straight-Line Pushing: Participants pushed the cart over a 4-meter straight path at a controlled speed.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Straight-Line Pulling: Participants pulled the cart over the same 4-meter path while maintaining a controlled speed.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Turning Maneuver: Both pushing and pulling tasks included a 90-degree turn to the left immediately after completing the straight-line segment (Figure 2).\u003c/p\u003e\n\u003cp\u003eThe order of handle designs was randomized to minimize potential bias. Each task (pushing and pulling) was repeated three times for every handle design, with adequate rest periods between trials to prevent fatigue. Participants were instructed to maintain a consistent posture and exertion level throughout the trials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Wrist Joint Angles: Wrist joint deviations, including flexion, radial deviation, and ulnar deviation, were measured using a digital angle measurement device.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Muscle Activity: EMG data were collected to measure average and peak muscle activation levels. All EMG signals were normalized to MVC for inter-subject comparison.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;Comfort questionnaire: Participants rated their 1-10 level of comfort using the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026middot; Data Processing: Wrist joint angles and EMG signals were processed using MATLAB (MathWorks, USA) to extract key metrics for analysis. EMG signals were filtered using a band-pass filter (20\u0026ndash;450 Hz) and normalized to MVC values.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Statistical Tests: Repeated measures analysis of variance (ANOVA) was applied to compare wrist joint angles and muscle activation levels across handle designs and task types (pushing vs. pulling). Duncan\u0026rsquo;s multiple range post-hoc test corrections were performed to identify significant differences between handle designs.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Significance Level: A p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Methodological Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure consistency and accuracy in data collection:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Participants underwent a short familiarization session before starting the experiment to reduce variability in movement patterns.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Electrode placement was verified before each trial to minimize signal noise.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Rest periods between trials were provided to prevent muscle fatigue from influencing the results.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Data collection was performed in a controlled laboratory setting with standardized environmental conditions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eWrist Joint Angles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the wrist joint angles (flexion, radial deviation, and ulnar deviation) measured under different handle types, movement paths, and force application directions. In contrast, Table 2 provides the statistical significance of these variables.\u003c/p\u003e\n\u003cp\u003eTable 1: Combined Results for Wrist Joint Angles, EMG Activity, and Subjective Ratings by Handle Design and Task\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHandle Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eTask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eFlexion (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eExtension (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eRadial Deviation (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eUlnar Deviation (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eBiceps Brachii (%MVC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eExtensor Carpi Radialis Longus (%MVC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eExtensor Carpi Ulnaris (%MVC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eComfort Score (1\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eRPE\u003c/p\u003e\n \u003cp\u003e(1\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePushing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.3 \u0026plusmn; 3.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.2 \u0026plusmn; 2.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.1 \u0026plusmn; 4.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.2 \u0026plusmn; 3.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34.2 \u0026plusmn; 5.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.1 \u0026plusmn; 4.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28.5 \u0026plusmn; 4.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.2 \u0026plusmn; 1.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.8 \u0026plusmn; 1.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.5 \u0026plusmn; 2.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.4 \u0026plusmn; 3.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.8 \u0026plusmn; 2.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.3 \u0026plusmn; 2.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.7 \u0026plusmn; 3.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18.3 \u0026plusmn; 2.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.7 \u0026plusmn; 3.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.8 \u0026plusmn; 1.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.5 \u0026plusmn; 1.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTurning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.2 \u0026plusmn; 4.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.5 \u0026plusmn; 3.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24.7 \u0026plusmn; 5.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.7 \u0026plusmn; 3.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.8 \u0026plusmn; 6.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.2 \u0026plusmn; 5.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33.1 \u0026plusmn; 5.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.9 \u0026plusmn; 1.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.5 \u0026plusmn; 1.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTurning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.8 \u0026plusmn; 3.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.3 \u0026plusmn; 4.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.5 \u0026plusmn; 4.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.5 \u0026plusmn; 3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38.5 \u0026plusmn; 5.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33.4 \u0026plusmn; 4.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31.2 \u0026plusmn; 5.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.5 \u0026plusmn; 1.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.9 \u0026plusmn; 1.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePushing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.3 \u0026plusmn; 2.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.8 \u0026plusmn; 2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.4 \u0026plusmn; 3.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.5 \u0026plusmn; 2.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.5 \u0026plusmn; 3.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18.8 \u0026plusmn; 3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.2 \u0026plusmn; 3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.5 \u0026plusmn; 1.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.8 \u0026plusmn; 1.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor wrist flexion, handle type had a highly significant effect (p \u0026lt; 0.001, Table 2), with handles A, B, and E producing the highest flexion angles (46.24\u0026deg;, 43.86\u0026deg;, and 41.33\u0026deg;, respectively). In contrast, handle C exhibited the lowest flexion angle (14.59\u0026deg;). Force application direction also significantly influenced wrist flexion (p = 0.001), with higher flexion observed in the push task (40.49\u0026deg;) compared to the pull task (32.05\u0026deg;). Additionally, there was a significant interaction between movement path and force application direction (p = 0.015), indicating that wrist flexion varied depending on whether pushing or pulling was performed in different movement conditions.\u003c/p\u003e\n\u003cp\u003eFor radial deviation, handle type had a highly significant effect (p \u0026lt; 0.001, Table 2). Handle A had the highest radial deviation (25.12\u0026deg;), while handle C had the lowest (1.14\u0026deg;). Force application direction also had a significant effect (p = 0.004), with greater radial deviation occurring in the push task (15.76\u0026deg;) than in the pull task (12.07\u0026deg;). Additionally, there were significant interactions between handle type and movement path (p = 0.002) and between handle type and force application direction (p = 0.012), indicating that wrist radial deviation varied depending on the combination of handle design and task conditions.\u003c/p\u003e\n\u003cp\u003eFor ulnar deviation, handle type had a highly significant effect (p \u0026lt; 0.001, Table 2), with handle C producing the highest ulnar deviation (23.18\u0026deg;) and handle A the lowest (0.78\u0026deg;). Movement path also significantly influenced ulnar deviation (p = 0.035), with higher deviation observed in the path with a turn (7.91\u0026deg;) compared to the straight path (6.74\u0026deg;). Additionally, there was a significant interaction between handle type and movement path (p = 0.002), suggesting that wrist ulnar deviation was influenced by both handle design and movement conditions.\u003c/p\u003e\n\u003cp\u003eInteraction Effects and Overall Trends\u003c/p\u003e\n\u003cp\u003eTable 2 also indicates a significant three-way interaction effect among handle type, movement path, and force application direction for radial deviation (p = 0.034) but not for flexion or ulnar deviation. The interaction between movement path and force application direction was significant for wrist flexion (p = 0.015), demonstrating that different movement paths altered the wrist posture depending on whether pushing or pulling motion was performed.\u003c/p\u003e\n\u003cp\u003eThe statistical results indicate that handle type is the primary factor affecting wrist joint angles (all p \u0026lt; 0.001). At the same time, movement path and force application direction contribute additional effects, particularly on radial and ulnar deviation. These findings underscore the importance of ergonomic handle design in minimizing extreme wrist postures and potential musculoskeletal strain.\u003c/p\u003e\n\u003cp\u003eTable 1. Wrist Joint angle value by handle type, movement path, and force application direction\u003c/p\u003e\n\u003cp\u003eUnit:\u0026deg;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7549%;\"\u003e\n \u003cp\u003eFlexion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7513%;\"\u003e\n \u003cp\u003eRadial deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5732%;\"\u003e\n \u003cp\u003eUlnar deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eHandle type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e46.24\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e25.12\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e0.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e43.86\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e13.23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e4.58\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e14.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e1.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e23.18\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e35.32\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e18.55\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e2.73\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e41.33\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e11.53\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e5.35\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eMovement path\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eStraight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e34.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e13.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e6.74a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003ePath with a turn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e37.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e14.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e7.91b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003eForce application direction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003ePush task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e40.49\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e15.76\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.9206%;\"\u003e\n \u003cp\u003ePull task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7549%;\"\u003e\n \u003cp\u003e32.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e12.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.5732%;\"\u003e\n \u003cp\u003e6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: a and b are Duncan\u0026rsquo;s group codes. Bold font indicates significant differences between the independent variables.\u003c/p\u003e\n\u003cp\u003eTable 2.Effect of variables on the Wrist Joint Angles and perceived exertion (p-values).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.70194%;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7513%;\"\u003e\n \u003cp\u003eFlexion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7513%;\"\u003e\n \u003cp\u003eRadial deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7513%;\"\u003e\n \u003cp\u003eUlnar deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0441%;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.70194%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0441%;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.70194%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0441%;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.70194%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0441%;\"\u003e\n \u003cp\u003eH\u0026acute;M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.70194%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0441%;\"\u003e\n \u003cp\u003eH\u0026acute;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.70194%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0441%;\"\u003e\n \u003cp\u003eM\u0026acute;\u0026nbsp;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.70194%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.0441%;\"\u003e\n \u003cp\u003eH\u0026acute;M\u0026acute;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.70194%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e\u003cs\u003e0.034*\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.7513%;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003e1. H: Handle type, M: Movement path, F: Force application direction\u003c/p\u003e\n\u003cp\u003e2.*, **, and ***indicate P \u0026lt; 0.05, P \u0026lt; 0.01, and P \u0026lt; 0.001, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMuscle Activity (EMG)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 presents the muscle activation levels (%MVC) across different handle types, movement paths, and force application directions, while Table 4 provides the statistical significance of these factors.\u003c/p\u003e\n\u003cp\u003eFor biceps brachii activation, handle type had a significant effect (p \u0026lt; 0.001, Table 4). Handles A, C, and D elicited higher activation levels (4.32%, 3.87%, and 4.41%, respectively), whereas handle B resulted in the lowest activation (2.76%). Movement path also had a significant effect (p = 0.001), with higher biceps activation occurring in the path with a turn (5.17%) compared to the straight path (2.42%). However, the direction of force application did not significantly affect the activation of the biceps brachii (p = 0.630). Additionally, there was a significant interaction between handle type and movement path (p = 0.013), indicating that certain handle designs led to varying levels of biceps activation depending on movement conditions.\u003c/p\u003e\n\u003cp\u003eFor triceps brachii activation, no significant main effects were found for handle type (p = 0.332) or movement path (p = 0.910, Table 4). However, force application direction had a significant impact (p = 0.015), with higher activation recorded during the push task (2.52%) compared to the pull task (2.21%). No significant interaction effects were observed for triceps brachii activity.\u003c/p\u003e\n\u003cp\u003eFor extensor carpi radialis longus activation, the movement path had a highly significant effect (p \u0026lt; 0.001, Table 4), with a greater activation level observed in the path with a turn (12.75%) compared to the straight path (7.92%). Handle type and force application direction did not significantly affect the activation of this muscle (p = 0.163 and p = 0.710, respectively). However, the interaction between handle type and movement path was significant (p = 0.006), suggesting wrist extensor activation varied across handle designs when navigating different movement paths.\u003c/p\u003e\n\u003cp\u003eFor extensor carpi ulnaris activation, all three independent variables had a significant effect (Table 4). Handle type influenced muscle activation (p = 0.036), with handle D producing the highest activation (8.65%) and handle E the lowest (6.54%). Movement path also had a significant impact (p = 0.017), with higher activation recorded in the path with a turn (8.13%) compared to the straight path (6.47%). Force application direction significantly influenced activation (p = 0.009), with the push task eliciting higher activation (8.02%) than the pull task (6.59%). Additionally, a significant interaction between handle type and movement path (p = 0.002) indicated that different handle designs influenced wrist extensor activation based on the movement trajectory.\u003c/p\u003e\n\u003cp\u003eInteraction Effects and Overall Trends\u003c/p\u003e\n\u003cp\u003eTable 4 indicates a significant two-way interaction between handle type and movement path for biceps brachii (p = 0.013), extensor carpi radialis longus (p = 0.006), and extensor carpi ulnaris (p = 0.002). However, there were no significant three-way interactions among handle type, movement path, and force application direction for any muscle group.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that movement path has the most substantial impact on muscle activation, particularly for wrist extensors and biceps brachii. At the same time, force application direction primarily influences triceps brachii and extensor carpi ulnaris activation. Handle type also plays a role in determining muscle load, particularly for biceps brachii and wrist extensors, and its effects are further modified by movement trajectory. These results highlight the importance of optimizing handle ergonomics to minimize muscle strain, especially in dynamic tasks requiring frequent directional changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eMuscle EMG value for by handle type, movement path, and force application direction\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Unit: %MVC\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eIndependent\u0026nbsp;variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 445px;\"\u003e\n \u003cp\u003eMuscle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eBiceps\u0026nbsp;brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTriceps\u0026nbsp;brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eExtensor carpi\u0026nbsp;radialis longus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eExtensor carpi\u0026nbsp;ulnaris\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHandle type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.32\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7.19\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e8.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.85\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.87\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7.29\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.41\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.63\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.54\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMovement path\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eStraight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.92\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePath with a turn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5.17\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12.75\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8.13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForce application direction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePush task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.52\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePull task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA and b are Duncan\u0026rsquo;s group codes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eEffect of variables on the muscle EMG activities and perceived exertion (p-values).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 331px;\"\u003e\n \u003cp\u003eMuscle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePerceived exertion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eBiceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eTriceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eExtensor carpi radialis longus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eExtensor carpi ulnaris\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.036\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eH\u0026acute;M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eH\u0026acute;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eM\u0026acute;\u0026nbsp;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eH\u0026acute;M\u0026acute;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eH: Handle type, M: Movement path, F: Force application direction\u003c/p\u003e\n\u003cp\u003e*, **, and ***indicate P \u0026lt; 0.05, P \u0026lt; 0.01, and P \u0026lt; 0.001, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjective Comfort Ratings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants rated Handle E as the most comfortable design, with an average score of 8.5 out of 10, followed by Handle B (7.8 out of 10). Handles A and C received the lowest ratings, averaging 5.2 and 4.9, respectively. These subjective ratings were consistent with the objective biomechanical findings, highlighting the ergonomic advantages of Handles B and E.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Findings\u003c/h2\u003e \u003cp\u003eThis study analyzed how handle type, movement path, and force application direction affect wrist angles, muscle activity, and user comfort. The results highlight handle type as the key factor in wrist posture, with significant differences in flexion, radial deviation, and ulnar deviation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Handles A, B, and E caused the highest flexion angles, while handle C had the lowest flexion but the highest ulnar deviation, increasing wrist fatigue risk. This is consistent with previous research suggesting that extreme wrist postures are associated with an increased likelihood of musculoskeletal disorders (MSDs) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eMovement path had the most significant impact on muscle activity, particularly affecting wrist extensors (extensor carpi radialis longus and extensor carpi ulnaris) and biceps brachii, with significantly higher activation occurring during turning maneuvers compared to straight-line movement (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This aligns with findings from Hoozemans[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], who reported that manual cart handling in dynamic environments places greater demands on upper limb muscles. Additionally, force application direction influenced specific muscle groups, with higher activation of the triceps brachii and extensor carpi ulnaris during pushing tasks (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, respectively), whereas pulling had a lesser impact.\u003c/p\u003e \u003cp\u003eFurthermore, interaction effects suggest that handle design must be evaluated under dynamic conditions. The interaction between movement path and force application direction significantly influenced wrist flexion (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). At the same time, the handle type significantly interacted with the movement path for radial and ulnar deviation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, respectively). These findings indicate that the influence of handle design is not fixed but varies based on task conditions, reinforcing the importance of dynamic assessments in ergonomic evaluations[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, these findings highlight the critical role of ergonomic handle design in minimizing extreme wrist postures and reducing muscle fatigue, thereby lowering the risk of musculoskeletal strain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Prior Work\u003c/h2\u003e \u003cp\u003ePrevious studies have demonstrated that handle design and movement conditions significantly impact upper limb strain, with excessive wrist flexion and ulnar deviation increasing the risk of musculoskeletal disorders (MSDs), such as carpal tunnel syndrome and tendinitis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The present study aligns with these findings, particularly in showing that handle C, which induced the highest ulnar deviation, may elevate the risk of wrist discomfort and injury.\u003c/p\u003e \u003cp\u003eMoreover, prior research has emphasized that pushing tasks require more significant muscle effort than pulling tasks, particularly for the triceps brachii and wrist extensors, consistent with our findings (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, unlike most previous studies primarily focused on static handle operations, this study further identifies significant interaction effects between handle type and movement path, particularly the increased muscle load during turning maneuvers. This underscores the need for ergonomic assessments to consider dynamic task conditions rather than relying solely on static posture evaluations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Practical Implications\u003c/h2\u003e \u003cp\u003eThe findings have important implications for reducing work-related musculoskeletal disorders (WMSDs), particularly in occupations requiring frequent manual cart handling, such as healthcare, logistics, and manufacturing. Hand design optimization can help reduce extreme wrist postures and reduce repetitive strain injuries (RSIs). Since handle C resulted in the highest ulnar deviation, similar designs should be avoided to reduce wrist strain. Additionally, given that wrist extensors and biceps brachii experience a higher workload during turning maneuvers, handle designs should aim to reduce grip force requirements and provide multiple grip positions to accommodate different movement patterns [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePushing tasks placed greater demands on the triceps brachii and extensor carpi ulnaris, suggesting that handle shape should distribute force more efficiently to reduce forearm strain. Ergonomic training should also focus on optimal force application techniques, as turning movements significantly increase muscle load, leading to fatigue accumulation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, subjective comfort ratings align with the biomechanical findings, showing that Handles B and E received the highest comfort scores while maintaining moderate muscle activation levels. This suggests that user comfort can serve as a reliable metric for ergonomic handle optimization[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These findings can be applied to medical carts, industrial equipment, and rehabilitation devices, allowing for ergonomic refinements based on both biomechanical efficiency and user preferences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eDespite the valuable insights provided by this study, certain limitations must be considered. The sample size was relatively small, with only 10 participants, which may limit the generalizability of the findings. Future research should include a larger and more diverse sample to validate these results. Additionally, this study was conducted in a single session, meaning that long-term fatigue effects were not assessed. Future research should examine prolonged use scenarios to evaluate cumulative strain risks.\u003c/p\u003e \u003cp\u003eFurthermore, this study focused specifically on nursing carts, whereas different industries may require varied handle designs. Future research should extend to medical devices, industrial machinery, and sports rehabilitation tools to assess ergonomic suitability across different applications. Investigating alternative handle geometries, such as angled or contoured grips, could further optimize handle design by reducing wrist strain while maintaining functional efficiency [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHandle type significantly impacts wrist joint angles and muscle activity, while movement path and force application direction further modify these effects. Ergonomic handle design is essential for reducing extreme wrist postures, minimizing muscle load, and enhancing user comfort. Integrating these insights into equipment design and workplace training can help reduce work-related musculoskeletal injuries, improving safety, efficiency, and worker well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there are no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethical statement is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the the\u0026nbsp;Ministry\u0026nbsp;of\u0026nbsp;Science\u0026nbsp;and Technology (MOST-108-2314-B-131-001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Dr. Yi-Lang Chen, Ming Chi University of Technology and Dr. Wen-Hsien Hsu, National Taiwan University Hospital for their advice on the present study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTrinkoff, A. 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Factors\u003c/em\u003e. \u003cb\u003e53\u003c/b\u003e (6), 740\u0026ndash;748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0018720811420256\u003c/span\u003e\u003cspan address=\"10.1177/0018720811420256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electromyography, ergonomic design, musculoskeletal strain, wrist joint angles, nursing cart","lastPublishedDoi":"10.21203/rs.3.rs-6226381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6226381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe ergonomic design of emergency nursing carts is crucial for reducing musculoskeletal strain during clinical tasks. This study evaluates how different handle designs affect muscle activity and wrist joint angles, aiming to identify an optimal design that enhances comfort and reduces strain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTen female participants performed straight-line pushing and turning tasks using five different nursing cart handle designs. Wrist joint angles\u0026mdash;including flexion, extension, radial deviation, and ulnar deviation\u0026mdash;were measured using a motion tracking system. Muscle activity in the biceps brachii, extensor carpi radialis longus, and extensor carpi ulnaris was recorded using electromyography (EMG). This study also discusses the limitations of EMG and motion tracking by comparing them with biomechanical measurement tools such as load cells, strain gauges, and subjective assessment tools like questionnaires and checklists. Data were analyzed for differences in muscle activation and wrist angle deviations across the handle designs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study found that wrist joint angles varied significantly across handle designs. One of the tested handle designs minimized extreme wrist positions, leading to lower flexion and radial deviation angles compared to other designs. Wrist joint angles differed significantly between handle designs. One design effectively minimized extreme wrist positions, reducing flexion and radial deviation. EMG analysis showed that some handle designs significantly lowered muscle activity across all muscle groups, indicating reduced strain during straight and turning tasks. Regarding user comfort, participants rated certain handle designs as the most comfortable, aligning with their superior ergonomic performance based on objective measures.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study demonstrates that handle design significantly affects wrist joint angles, muscle activity, and perceived comfort during cart operation. A particular handle design, characterized by its ability to minimize musculoskeletal strain, offers a promising ergonomic improvement for emergency nursing carts. The findings also highlight how improved ergonomic design can contribute to better healthcare efficiency and potentially enhance patient care by reducing fatigue-related errors.\u003c/p\u003e","manuscriptTitle":"Handle Design Matters: Biomechanical Evaluation of Nursing Carts Using Electromyography and Wrist Angles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 09:13:17","doi":"10.21203/rs.3.rs-6226381/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-18T11:00:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T03:31:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309418408991122733964822313436494278064","date":"2025-06-02T12:14:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-31T13:45:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310647374070531453171331567505919073756","date":"2025-04-20T03:11:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-19T10:55:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200851349384375821911641329667501399217","date":"2025-04-15T02:47:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-24T00:51:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-24T00:50:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-18T06:06:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-17T06:27:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-14T12:31:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a562f57-86db-4c2e-aff9-0bd7a076b473","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46398764,"name":"Biological sciences/Biological techniques/Behavioural methods"},{"id":46398765,"name":"Physical sciences/Engineering/Biomedical engineering"},{"id":46398766,"name":"Physical sciences/Physics/Techniques and instrumentation/Design synthesis and processing"}],"tags":[],"updatedAt":"2025-11-03T16:00:45+00:00","versionOfRecord":{"articleIdentity":"rs-6226381","link":"https://doi.org/10.1038/s41598-025-21820-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-30 15:57:04","publishedOnDateReadable":"October 30th, 2025"},"versionCreatedAt":"2025-04-03 09:13:17","video":"","vorDoi":"10.1038/s41598-025-21820-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-21820-x","workflowStages":[]},"version":"v1","identity":"rs-6226381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6226381","identity":"rs-6226381","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}