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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. 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, triceps brachii, extensor carpi radialis longus (ECR), and extensor carpi ulnaris (ECU) 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. 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. Handle type C 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. This study provides initial evidence that handle design may influence wrist joint angles, muscle activity, and perceived comfort during cart operation. A particular handle design, characterized by its ability to minimize musculoskeletal strain, offering a potential ergonomic improvement for emergency nursing carts, which warrants further investigation. The findings also highlight how improved ergonomic design can contribute to better healthcare efficiency and potentially enhance patient care by reducing fatigue-related errors.
Introduction
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 research1,2. Figure 1 shows a standard crash cart commonly used in most hospitals.
Fig. 1 [Images not available. See PDF.]
A standard crash cart.
Several studies have examined hospital cart mobility, focusing on push/pull forces, wheel configurations, and load distribution. For example, studies by Rousek and Hallbeck3 and Muzammil et al.4 analyzed hospital cart mobility by examining push/pull force requirements, wheel configuration effects, and load balancing strategies. However, these studies primarily focused on cart mechanics and did not assess the biomechanical strain placed on healthcare workers. 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 activity5, 6–7.
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 issues8. 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 movements9,10.
Research has shown that wrist deviations beyond 15°–20° increase the risk of chronic musculoskeletal disorders, particularly when combined with sustained muscle exertion11. 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 discomfort12.In contrast, other researchers have used surface EMG to evaluate muscle fatigue and activation in manual handling tasks13. These studies highlight the value of EMG for assessing upper limb strain in repetitive tasks, but few have applied this technique in the context of nursing cart ergonomics. These findings emphasize the need for an ergonomic redesign of crash cart handles to prevent long-term injuries.
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 safety14. 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 carts7,15.
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 setting7,16,17. 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.
The objectives of this study are threefold. First, it aims to compare wrist joint deviations and forearm muscle activation patterns across five distinct nursing cart handle designs during straight pushing, pulling, and turning tasks. Second, the study seeks to identify handle configurations that can effectively minimize musculoskeletal strain and perceived exertion among healthcare workers. Finally, based on these findings, the study intends to provide ergonomic design guidance that can inform the future optimization of crash cart handles, with the goal of enhancing usability and reducing the risk of occupational injuries. By addressing these objectives, the study contributes a quantitative framework for evaluating ergonomic handle designs in hospital carts and helps bridge the gap between biomechanical research and practical applications in clinical environments.
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. This study was approved by the Institutional Review Board of Taipei Medical University (TMU IRB, Approval No. 2021040001). 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 2 shows the specifications of the five handles tested.
Fig. 2 [Images not available. See PDF.]
The specifications of handles used in this study.
The dimensions of the cart and handle components used in this study were based on direct field observations of standard crash carts commonly used in Taiwanese hospitals, as well as measurements taken from commercially available shopping carts and food service carts used on the Taiwan High Speed Rail. These carts were selected for reference because they are designed for frequent push–pull operations and must meet practical ergonomic requirements in high-use environments. The handle diameters, ranging from 3.0 to 3.5 cm, were aligned with ergonomic literature, which suggests that an average diameter of 3.2 cm provides optimal grip comfort and control for adult users18. All five handle prototypes were constructed using stainless steel tubing as the structural base, consistent with commonly used materials in hospital crash carts. Customized grip covers were produced using 3D-printed ABS (Acrylonitrile Butadiene Styrene) plastic to simulate different ergonomic grip shapes and mounted onto the stainless steel tubes. The surface texture of the grip area was hard and matte-finished.
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 the nursing staff are female. According to previous studies, women constitute approximately 90% of the nursing workforce. A World Health Organization (2020) study 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 better19.
Experimental setup
The experimental setup included a standardized nursing cart and five distinct handle designs labeled A through E. Each handle varied in height, shape, and orientation to represent a range of ergonomic configurations. The nursing cart used in the study weighed 50 kg, which reflects typical clinical conditions encountered during daily hospital operations. During all tasks, participants were instructed to maintain a natural and consistent grip force. The standardized verbal instruction was: “Hold the handle firmly, as if pushing a cart during routine hospital work,” to ensure uniformity in grip behavior across participants.
Measurement instruments
Electromyography (EMG): surface electrodes MA300-XII (Motion Lab Systems) were placed on the biceps brachii, triceps brachii, ECR, and ECU muscles to measure muscle activity. These muscles were selected due to their involvement in pushing, pulling, and wrist stabilization movements.
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.
MVC was recorded for each muscle using a standardized protocol according to Hislop, Avers, and Brown20. 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.
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
Before beginning the experiment, each participant was given a detailed verbal explanation of the entire protocol to ensure they fully understood the procedure. A short familiarization session was conducted, during which participants performed stretching exercises for at least five minutes to reduce injury risk and promote consistent movement patterns throughout the trials. Anthropometric data including height, weight, shoulder height, elbow height, knuckle height, and knee height were recorded to account for physical variation among participants.
Surface electrodes were placed on the biceps brachii, triceps brachii, extensor carpi radialis longus (ECR), and extensor carpi ulnaris (ECU) of the dominant hand to monitor muscle activity during cart operation. The EMG signals were band-pass filtered (20–450 Hz), full-wave rectified, and normalized to each participant’s maximum voluntary contraction (MVC). MVC testing followed standardized procedures outlined by Hislop, Avers, and Brown20, where participants performed three five-second isometric contractions against manual resistance. The highest one-second average was used for normalization. All procedures adhered to SENIAM recommendations to ensure EMG signal reliability and comparability across participants.
Participants were instructed to grip the handle naturally and consistently throughout the experiment. Specifically, they were told: “After hearing the bell, grip the handle firmly for about one second, then begin the push or pull task as you would during routine hospital work.” During each trial, the participant pushed or pulled a 50-kg nursing cart along a straight 4-meter path or a path involving a 90-degree left turn. A metronome set at 80 beats per minute was used to standardize movement speed. Each task—straight-line pushing, pulling, and turning—was repeated three times for each of the five handle designs, and the order of handle use was randomized to reduce order effects. Three-minute rest periods were provided between trials to minimize fatigue.
After completing the EMG-based trials, participants repeated the same tasks with wrist angle sensors attached. A digital goniometer was used to record wrist flexion, extension, radial deviation, and ulnar deviation. The device was calibrated before each session, and measurements followed the wrist posture risk thresholds defined in prior research21.
After each handle trial, participants completed a 10-point comfort questionnaire to subjectively evaluate each handle’s usability. All data were collected in a controlled laboratory setting under standardized lighting, temperature, and noise conditions.
Task protocol
Participants performed pushing and pulling tasks for each handle design under control conditions:
Straight-line pushing: participants pushed the cart over a 4-m straight path at a controlled speed.
Straight-line pulling: participants pulled the cart over the same 4-m path while maintaining a controlled speed.
Turning maneuver: both pushing and pulling tasks included a 90-degree turn to the left immediately after completing the straight-line segment (Fig. 3).
Fig. 3 [Images not available. See PDF.]
Illustrates the direction and dimension of the paths. Carts were pushed and pulled in a straight line or with a 90° left turn.
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
Wrist joint deviations, including flexion, radial deviation, and ulnar deviation, were measured using a calibrated digital angle measurement device. Muscle activity was assessed by collecting surface electromyography (EMG) data, which provided both average and peak activation levels for each targeted muscle. All EMG signals were normalized to each participant’s maximum voluntary contraction (MVC) to allow for consistent inter-subject comparisons. Following the completion of each trial, participants were asked to rate their perceived comfort for the tested handle using a 10-point questionnaire, with higher scores indicating greater comfort.
Statistical analysis
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. 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. Level: A p value < 0.05 was considered statistically significant.
Additional methodological details
To ensure consistency and accuracy in data collection, several procedural controls were implemented. Each participant completed a brief familiarization session prior to the experiment to reduce variability in movement patterns and improve task consistency. Electrode placement was carefully verified before each trial to minimize signal noise and ensure accurate EMG readings. Adequate rest periods were incorporated between trials to prevent muscle fatigue from affecting performance and measurement validity. All experimental procedures were conducted in a controlled laboratory environment with standardized lighting, temperature, and ambient noise to maintain consistent testing conditions throughout the study.
Results
The ten female participants had a mean age of 28.1 years (SD = 5.4), a mean height of 160.9 cm (SD = 4.2), and a mean body mass index (BMI) of 21.4 kg/m2 (SD = 2.1). All participants were right-handed and had no history of upper extremity musculoskeletal disorders. Prior to statistical analysis, a Shapiro–Wilk test was conducted to assess the normality of all dependent variables, including wrist joint angles and EMG values. The test results confirmed that the data were normally distributed, and therefore no outliers were removed.
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. Wrist joint angles (°) in flexion, radial deviation, and ulnar deviation during crash cart manoeuvring by handle type, movement path, and force application direction.
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 | 0.477 |
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.
Based on ergonomic guidelines21, wrist extension should not exceed 30°, flexion should not exceed 45°, and radial and ulnar deviation should remain within 18° to reduce the risk of musculoskeletal strain. 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 a 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.
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. Sustained muscle activity over 15% MVC is associated with greater fatigue risk.
Table 3. Mean normalised EMG values (%MVC) of upper extremity muscles (biceps brachii, triceps brachii, extensor carpi radialis longus, and extensor carpi ulnaris) during crash cart manoeuvring, stratified by handle type, movement path, and force application direction.
Independent variable | Muscle | |||
|---|---|---|---|---|
Biceps brachii | Triceps brachii | Extensor carpi radialis longus | Extensor carpi ulnaris | |
Handle type | ||||
A | 4.32b | 2.72 | 11.73 | 7.19ab |
B | 2.76a | 2.25 | 8.12 | 6.85ab |
C | 3.87b | 2.18 | 10.45 | 7.29ab |
D | 4.41b | 2.50 | 10.22 | 8.65b |
E | 3.63ab | 2.16 | 11.16 | 6.54a |
Movement path | ||||
Straight | 2.42a | 2.37 | 7.92a | 6.47a |
Path with a turn | 5.17b | 2.35 | 12.75b | 8.13b |
Force application direction | ||||
Push task | 3.94 | 2.52b | 10.24 | 8.02b |
Pull task | 3.65 | 2.21a | 10.43 | 6.59a |
A and b are Duncan’s group codes.
Table 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.
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 (pp = 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 (pp = 0.630). Additionally, there was a significant interaction between handle type and movement path (pp = 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 (pp = 0.332) or movement path (pp = 0.910, Table 4). However, force application direction had a significant impact (pp = 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 (pp = 0.163 and pp = 0.710, respectively). However, the interaction between handle type and movement path was significant (pp = 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 (pp = 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 (pp = 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 (pp = 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 (pp = 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 (pp = 0.013), extensor carpi radialis longus (p = 0.006), and extensor carpi ulnaris (pp = 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.
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 (pp < 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)13,22
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 Hoozemans23, 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 =p = 0.015 and p =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 evaluations24.
Overall, these findings suggest that ergonomic handle design may play an important role in minimizing extreme wrist postures and reducing muscle fatigue, though further studies are needed to confirm these effects in real-world settings.
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 tendinitis13. 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)23. 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 evaluations24,25.
Clinical and practical implications
The findings provide preliminary insights that may inform strategies to reduce work-related musculoskeletal discomfort, 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 patterns26.
Pushing tasks showed slightly higher activation of the triceps brachii and extensor carpi ulnaris compared to pulling, indicating that certain handle shapes may influence muscle recruitment patterns in the upper limb. While the differences were modest, they were consistent across participants. Therefore, ergonomic training should also focus on optimal force application techniques, as turning movements significantly increase muscle load, leading to fatigue accumulation23.
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 optimization27. 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 efficiency28.
Conclusion
This study demonstrated that nursing cart handle design has a significant impact on wrist joint angles, forearm muscle activation, and perceived comfort during simulated pushing and turning tasks. By evaluating five handle configurations, we identified that design characteristics such as grip orientation and elevation substantially influence ergonomic performance. Specifically, Handle E exhibited the most favorable biomechanical and subjective outcomes, while designs inducing extreme flexion or ulnar deviation, such as Handle C, may increase musculoskeletal strain.
These findings offer a foundational framework for future ergonomic interventions in hospital equipment design. Rather than asserting direct clinical risk, this study provides preliminary evidence linking handle design to biomechanical exposure during typical nursing tasks. The results support the integration of ergonomic principles in crash cart development to enhance user comfort, reduce cumulative fatigue, and improve overall healthcare safety.
Acknowledgements
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.
Author contributions
The author, Ding-Yang Hsu, is the sole contributor to this manuscript. All aspects of the study, including conception, design, data collection, analysis, interpretation, and manuscript preparation, were conducted exclusively by the author.
Funding
This work was supported by the Ministry of Science and Technology (MOST-108-2314-B-131-001).
Data availability
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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