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Article

Occupation-Related Musculoskeletal Disorders Among Watermelon Farmers in Taiwan: A Cross-Sectional Study

by
Shinhao Yang
1,*,
Chi-Yu Chuang
2,3,*,
Kun-Che Lee
4,
Hsiao-Chien Huang
1,
Ying-Fang Hsu
1,
Chun-Yao Wang
1 and
Chiou-Jong Chen
2
1
Environmental Sustainability Lab, Center for General Education, CTBC Business School, Tainan 709, Taiwan
2
Department of Occupational Safety and Health, Chang Jung Christian University, Tainan 711, Taiwan
3
Center of Corporate ESG and Sustainability Research, Chang Jung Christian University, Tainan 711, Taiwan
4
Institute of Labor, Occupational Safety and Health, Ministry of Labor, New Taipei City 221, Taiwan
*
Authors to whom correspondence should be addressed.
Occup. Health 2026, 1(3), 27; https://doi.org/10.3390/occuphealth1030027
Submission received: 11 March 2026 / Revised: 13 June 2026 / Accepted: 25 June 2026 / Published: 29 June 2026
Versions Notes

Abstract

This study employed a quantitatively driven mixed-methods approach to investigate crop-specific musculoskeletal disorder (MSD) prevalence and ergonomic risks among Taiwanese watermelon farmers, comparing them with pear (canopy-based) and pineapple (static-stooping) cohorts. A total of 218 participants were recruited (60 watermelon, 60 pear, 63 pineapple, and 35 non-farmers). Structured questionnaires quantified MSD prevalence and ergonomic exposures, while qualitative interviews provided a supportive operational context. Watermelon farmers reported a prominent lower-limb dominant discomfort profile, with a hip/thigh disorder prevalence (36.7%) significantly higher than pear (13.1%) and pineapple (11.1%) farmers. Multivariate logistic regression showed that daily working hours (aOR = 1.38) and uncomfortable posture duration (aOR = 1.33) were independent predictors of hip/thigh disorders. This elevated prevalence may be associated with the combined effects of prolonged deep squatting, dynamic heavy lifting, and unstable sandy terrain. Furthermore, low personal protective equipment adoption was primarily related to environmental incompatibility (sand accumulation and thermal stress). Although the cross-sectional design limits causal inferences, these findings highlight the need for targeted, crop-specific ergonomic interventions, such as breathable, sand-resistant joint supports.

1. Introduction

Agricultural work involves various occupational hazards [1,2,3,4], including physical [5,6], chemical [7,8], biological [9,10], and ergonomic stressors [11]. Among these, work-related musculoskeletal disorders (MSDs) are a leading cause of disability in the agricultural workforce [12,13,14,15]. Epidemiological studies indicate that the prevalence of MSDs among farmers is significantly higher than in the general population [16,17,18]. These disorders are frequently associated with task-specific risk factors such as prolonged static postures [19,20], excessive force exertion [13,21], repetitive motions [12,22], and whole-body vibration [16,23].
While occupational risks in canopy-based orchards have been well-documented [24,25,26,27]—where upper-extremity and spinal strains are common due to overhead reaching and ladder climbing [28,29,30]—ground-level creeping crops are less studied. In Taiwan, watermelon cultivation typically occurs on open sandy riverbeds, presenting specific ergonomic challenges. Walking on yielding sandy terrain requires greater metabolic energy and muscular effort compared to solid ground [31]. This unstable surface requires continuous compensatory stabilization from the lower limbs [32], which can alter gait kinematics and potentially increase the risk of lower-extremity morbidity [33,34].
Ergonomically, watermelon cultivation requires prolonged deep squatting during planting and care [35]. Different ground-level postures have distinct biomechanical impacts. While stooping (bending forward at the waist) mainly increases shear and compressive forces on the lumbar spine [35], deep squatting involves high hip and knee flexion. This posture can place compressive loads on the knee joints and requires sustained isometric contraction of the lower-limb muscles [36,37,38]. Additionally, watermelon harvesting involves dynamic heavy lifting and tossing [11,39], contrasting with the repetitive wrist motions seen in pineapple farming [40]. The combination of unstable sandy terrain and heavy physical tasks may compound biomechanical stress on the lower extremities [41,42].
Therefore, this study aimed to investigate the crop-specific biomechanical risks and MSD profiles among Taiwanese watermelon farmers. Using a comparative cross-sectional design, we compared the MSD prevalence of watermelon farmers to three reference groups: pear farmers (representing overhead tasks) [24,42], pineapple farmers (representing ground-level stooping), and non-farming controls (accounting for age-related degeneration) [17,43]. By quantifying daily ergonomic exposures and assessing MSD profiles with the standardized Nordic Musculoskeletal Questionnaire (NMQ) [44], this study clarifies how specific agricultural environments and tasks may contribute to MSD development, providing an evidence base for targeted ergonomic interventions.

2. Materials and Methods

2.1. Study Design and Setting

This study employed a quantitatively driven mixed-methods design, primarily utilizing a comparative cross-sectional approach to investigate crop-specific musculoskeletal risks. Within this framework, the qualitative component played a supportive role, functioning specifically to provide operational context and clarify the daily ergonomic exposures underlying the quantitative findings. Reporting follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The research was conducted in three major agricultural hubs in Taiwan representing specific cultivation modalities: Erlun Township in Yunlin County (watermelon), Dongshih District in Taichung City (high-grafted pear), and Neipu Township in Pingtung County (pineapple). These regions were selected for their high production density of the respective crops and specific geographic characteristics, particularly the open sandy riverbeds in Erlun Township.

2.2. Participants

A total of 218 participants were recruited through purposive sampling in collaboration with local farmers’ associations. Inclusion criteria required participants to be active, full-time farmers who identified the specified crop (watermelon, pear, or pineapple) as their primary occupation, with at least one year of cultivation experience. Recruitment involved informational sessions and direct outreach, enrolling all eligible volunteers. The study population was stratified into four groups to isolate specific ergonomic risks:
(1)
Watermelon farmers (n = 60, target group): cultivating ground-level creeping crops on sandy riverbeds.
(2)
Pear farmers (n = 60, reference group): performing overhead work on high trellis systems requiring arm elevation and ladder climbing [24,42].
(3)
Pineapple farmers (n = 63, reference group): performing ground-level work primarily involving static stooping, contrasting with the deep squatting and dynamic lifting seen in watermelon farming [39].
(4)
Non-farmers (n = 35, control group): residents from the same rural communities, serving as a baseline for general musculoskeletal degeneration [17,43].
A post hoc power analysis using G*Power software (Version 3.1.9.7, Heinrich-Heine-Universität Düsseldorf, Germany) verified the adequacy of the sample size. Given the total sample of 218 participants, an alpha level of 0.05, and a medium effect size (ρ = 0.3), the achieved statistical power was 0.993. This value significantly exceeds the conventional 0.80 threshold, confirming that the sample was sufficient to reliably detect occupational differences across the cohorts.

2.3. Instruments

Data were collected using a structured questionnaire comprising four sections: demographics, quantitative ergonomic risk exposure, personal protective equipment (PPE) usage, and musculoskeletal symptoms. To ensure content validity, the instrument was reviewed by an expert panel of four occupational health scholars. The questionnaires were primarily administered by trained personnel during organized sessions to facilitate consistent understanding and minimize reporting errors.
(1)
Demographic Data: This section recorded age, gender, height, weight, farming seniority, and average daily working hours. Body mass index (BMI) was calculated from self-reported height and weight.
(2)
Quantitative Ergonomic Risk Exposure: Adapted from the Taiwan Ministry of Labor (ILOSH) agricultural hazard frameworks, this tool was designed to quantify the daily duration (hours/day) of physical stressors. Participants estimated their average daily exposure to four specific risk factors: prolonged fixed postures, awkward postures (e.g., deep squatting, kneeling, or severe stooping), heavy lifting (loads > 10 kg), and repetitive motions.
(3)
PPE Usage: Participants reported their regular use of specific protective gear, defined as consistent wear during more than 70% of cumulative working hours per shift. Evaluated items included hats, eye protection, knee pads, and back supports.
(4)
Musculoskeletal Symptoms: The 12-month prevalence of MSDs across nine anatomical regions was assessed using the Standardized Nordic Musculoskeletal Questionnaire (NMQ). The NMQ is an established epidemiological instrument with documented test–retest reliability and clinical validity [45].

2.4. Ethical Considerations

The study protocol was approved by the Governance Framework for Human Research Ethics at National Cheng Kung University (Protocol Code: 112147; approved on 24 April 2023). The study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to enrollment, and data confidentiality and anonymity were strictly maintained.

2.5. Statistical Analysis

Data analyses were performed using Stata 14.0 MP. Descriptive statistics are presented as means ± standard deviations (SDs) for continuous variables and frequencies (percentages) for categorical variables.
Group differences were analyzed using one-way analysis of variance (ANOVA) with Tukey’s HSD post hoc test for continuous variables (e.g., daily duration of ergonomic exposures) and the chi-square test for categorical variables (e.g., MSD prevalence, PPE usage).
To identify independent predictors of MSDs, a multivariate logistic regression model was constructed. Variable inclusion was determined through preliminary bivariate screening to prevent overfitting. Variables demonstrating significant correlations (p < 0.05) in initial point-biserial analyses, alongside established epidemiological confounders (age, gender, BMI), were included. Multicollinearity was assessed using the Variance Inflation Factor (VIF), with a threshold of VIF < 5 indicating the absence of severe collinearity. The final regression model adjusted for age, gender, BMI, daily working hours, and the duration of uncomfortable postures. Statistical significance was set at p < 0.05.

2.6. Qualitative Inquiry

To support the primary quantitative analysis and contextualize the findings, semi-structured interviews were conducted with seven watermelon farmers (Cases 01–07). Participants were recruited via snowball sampling, initiated by local agricultural groups. Purposive selection targeted experienced, full-time farmers (ages 30–69) actively experiencing musculoskeletal or ocular symptoms, providing information-rich cases regarding daily operational realities.
The interview guide was based on Ergonomic Work Analysis (EWA) principles, focusing on three dimensions: physical task lifecycles, geomorphological constraints (sandy riverbeds), and sociocultural barriers to personal protective equipment (PPE) adoption. Qualitative data were evaluated using the six-phase thematic analysis framework by Braun and Clarke [46]. Transcripts were inductively coded and clustered into higher-order themes.
Two researchers independently coded the data to ensure reliability, resolving discrepancies through consensus meetings until an inter-rater agreement of >90% was achieved. Because the interviews were originally conducted in Traditional Chinese, the qualitative statements presented in this study are translated excerpts from individual verbatim transcripts. The translation process was verified by the research team to ensure semantic accuracy.

3. Results

3.1. Demographic Characteristics

Table 1 presents the demographic and operational characteristics of the 218 participants across the four study groups. Significant differences existed among the groups regarding age and gender (p < 0.001). Specifically, pineapple farmers were younger and included a higher proportion of females compared to the older, male-dominated watermelon and pear cohorts. Body mass index (BMI) and educational attainment did not differ significantly among the groups (p > 0.05). For operational traits, farming seniority differed significantly among the agricultural groups (p < 0.001), whereas daily working hours did not (p = 0.082). Due to these baseline differences, age and gender were included as adjusting covariates in all multivariate regression models to control for potential confounding.
Additionally, socioeconomic and operational factors varied significantly among the cohorts. For instance, pineapple farmers reported the highest agricultural mechanization usage (2.5 ± 0.7 h/day) and the highest prevalence of previous musculoskeletal injuries (79.4%). Conversely, non-farmers reported the highest proportion of regular leisure physical activity (51.4%).

3.2. Ergonomic Risk Exposure Profiles

Daily ergonomic exposures demonstrated task-specific physical demands among the farming groups (Table 2). Pear and pineapple farmers reported significantly longer daily durations of prolonged fixed postures compared to watermelon farmers (p = 0.010). Exposure to awkward postures also varied significantly across groups (p = 0.013), with pineapple farmers reporting the highest duration, significantly exceeding that of pear farmers. Watermelon farmers also reported substantial exposure to these awkward postures. In contrast, the daily durations for heavy lifting and repetitive motions did not differ significantly among the cohorts (p > 0.05). This indicates that heavy lifting and repetitive motions are general agricultural hazards, regardless of the crop type.

3.3. Prevalence of Musculoskeletal Disorders

The prevalence of MSDs varied significantly across anatomical regions among the cohorts (Table 3). Watermelon farmers reported a prominent lower-limb dominant discomfort profile, with a hip/thigh disorder prevalence of 36.7%. This was significantly higher than in pear (13.1%) and pineapple (11.1%) farmers (p < 0.001). Although knee complaints were numerically highest among watermelon farmers (40.0%), a high prevalence of knee pain was common across all farming groups compared to the non-farming controls, without significant cross-group differences (p = 0.321).
For upper-limb and axial regions, pear farmers reported the highest prevalence of shoulder (42.6%) and upper back (39.3%) disorders (p < 0.001), consistent with the biomechanical demands of overhead work. Significant differences were also observed for the elbows (p = 0.024), wrists/hands (p = 0.039), lower back (p = 0.048), and ankles/feet (p = 0.007). Post hoc comparisons indicated that upper-extremity and spinal discomfort (wrists/hands and lower back) primarily differentiated pear and pineapple farmers from non-farmers. In contrast, lower-extremity issues (ankles/feet) significantly separated ground-level workers (watermelon and pineapple farmers) from the control group, highlighting the specific physical demands of different agricultural environments.

3.4. Risk Factor Analysis

Bivariate correlation analysis was used as an initial screening tool to identify candidate predictors of MSDs (Table 4). Hip/thigh disorders correlated positively with both daily working hours and the duration of uncomfortable postures (p < 0.05). Additionally, repetitive motion correlated significantly with elbow and knee complaints (p < 0.05), whereas age correlated negatively with wrist/hand and knee disorders (p < 0.05).
Multivariate logistic regression models were constructed to identify independent risk factors and control for confounders (Table 5). Variance Inflation Factor (VIF) diagnostics confirmed the absence of severe multicollinearity among the predictors (mean VIF = 1.38, maximum VIF = 2.14). Regression analysis showed that pear farming remained a significant independent predictor for upper back disorders compared to pineapple farming, even after adjusting for ergonomic exposures. For hip/thigh disorders, the effect of watermelon farming became non-significant after including ergonomic variables. Instead, hip/thigh discomfort was independently associated with increased daily working hours and longer durations of uncomfortable postures. These findings suggest that the high prevalence of lower-limb symptoms among watermelon farmers may be more closely associated with cumulative physical workload—specifically, extended durations in awkward postures—than with the crop category alone.

3.5. Qualitative Insights: Operational Realities and Constraints

Interviews with the seven watermelon farmers provided operational context regarding how environmental factors and task demands influence their work behaviors and risk exposures.
  • Theme 1: Biomechanical Burden of Ground-Level Tasks
Farmers consistently identified ground-level tasks—particularly during planting, weeding, and harvesting—as the most physically demanding aspects of their work. Because watermelon farming on riverbeds lacks posture-supporting tools, it requires continuous direct ground-level interaction.
“Planting seedlings, weeding, and selecting fruit all require bending over. During harvest, we have to bend down to pick the melons and then carry them… heavy loads on a soft, sandy surface make it even harder on the waist.”
(Representative excerpt, Case 01)
  • Theme 2: Environmental Hazards from Sandy Terrain
The sandy riverbed terrain was described not only as an ergonomic challenge but also as a direct source of ocular distress. This is particularly prevalent during winter soil preparation when seasonal winds increase airborne dust.
“When we prepare the land in winter, the wind blows the sand and dust everywhere. It gets into our eyes and makes them inflamed and painful. The sun is also very strong because there is no shade on the riverbed.”
(Representative excerpt, Case 03)
  • Theme 3: Barriers to Protective Equipment Adoption
While farmers universally utilized sun protection for their heads (e.g., broad-brimmed hats, scarves), the use of biomechanical supports (such as knee pads and back belts) was notably absent, despite high rates of reported joint pain. Interviews indicated that such gear is often impractical and restricts mobility in hot, sandy environments.
“We wear hats and scarves to block the sun and sand, but for the back and knees, we mostly just endure the pain or seek medical treatment when it gets unbearable.”
(Representative excerpt, Case 02)

4. Discussion

4.1. Distinct MSD Profiles: The “Lower-Limb Dominant” Pattern

Comparing the different agricultural cohorts highlights the potential biomechanical implications of distinct ground-level postures. Pineapple cultivation primarily involves static stooping (trunk flexion), which is biomechanically associated with increased lumbar shear forces—consistent with the high prevalence of lower back pain observed in this group [35]. In contrast, watermelon farming requires prolonged deep squatting (averaging 2.00 h/day). Existing biomechanical literature suggests that deep squatting involves extreme knee and hip flexion, potentially increasing patellofemoral compressive forces and requiring sustained isometric contraction of the quadriceps [36,37,38]. This aligns with epidemiological evidence linking occupational squatting to lower-limb joint degeneration [21]. Furthermore, qualitative data (Theme 1) indicated that deep squatting is necessary for tasks such as seedling and fruit selection. These findings suggest that the specific type of ground-level posture (squatting versus stooping)—rather than just the duration of exposure—may differentially influence localized joint strain.

4.2. The Synergistic Effect of Long Working Hours

This study identified daily working hours as an independent predictor of lower-limb MSDs. In the multivariate logistic regression, the adjusted odds ratio (aOR) for hip/thigh disorders was 1.38 for each additional hour worked. Although average daily working hours did not differ significantly among the farming groups (p = 0.082), the regression analysis underscores the association between prolonged work duration and joint discomfort.
According to fatigue-failure theory, musculoskeletal tissues require adequate recovery time following mechanical stress [15]. Our qualitative findings (Theme 1) provide operational context for this mechanism, as farmers reported working extended hours during harvest seasons to meet market demands. The combination of prolonged work durations and demanding ground-level postures (e.g., deep squatting) may exceed the physiological recovery capacity of the lower-limb joints. Farmers’ accounts of enduring pain during these intensive shifts suggest that extended exposure without sufficient recovery potentially contributes to cumulative musculoskeletal strain.

4.3. Environmental Impact: The “Unstable Ground” Factor

The specific cultivation environment of watermelon farming—open-field sandy riverbeds—acts as a significant environmental risk factor. Previous studies indicate that walking on yielding sand significantly increases metabolic energy cost and muscular effort [31]. Consequently, the lower limbs may require increased muscle co-contraction to maintain postural equilibrium [32,33,34], providing a plausible biomechanical context for the higher prevalence of lower-limb MSDs. Furthermore, qualitative data (Theme 2) show that this environmental impact extends beyond mechanical stress. Farmers reported that seasonal winds cause severe sand accumulation, leading to ocular distress and inflammation. These harsh environmental conditions may prompt workers to adopt asymmetric or guarded postures for self-protection, further altering neutral biomechanics during manual tasks.

4.4. The Gap Between Risk and Protection

Despite the identified biomechanical risks, the use of personal protective equipment (PPE) was low, with only 5.0% of watermelon farmers using knee pads and 6.7% using back supports (Table 6). Qualitative data (Theme 3) indicate that this low adoption stems primarily from environmental incompatibility rather than a lack of awareness. Farmers reported that knee pads trap abrasive sand and back supports cause heat stress in unshaded fields. Therefore, future ergonomic interventions—such as passive exoskeletons—should incorporate breathable, sand-resistant materials. Following the hierarchy of controls, preventive strategies should prioritize engineering modifications (e.g., wide-tired transport aids) and administrative controls (e.g., strategic shift scheduling). Additionally, agricultural agencies should develop crop-specific guidelines to promote these interventions.
To translate these observational findings into clinical practice, crop-specific physiotherapy and occupational prevention strategies are recommended. For watermelon farmers, who experience high rates of lower-limb discomfort associated with deep squatting and yielding terrain, physiotherapy should prioritize lower-limb stabilization, quadriceps endurance, and proprioceptive training. Conversely, for pineapple farmers affected by prolonged stooping, rehabilitation should emphasize lumbar extensor conditioning and core stability. For occupational prevention, administrative controls like task rotation can help alternate biomechanical loads. Furthermore, ergonomic interventions must account for environmental constraints, suggesting the use of long-handled tools to reduce trunk flexion in pineapple fields and sand-resistant joint supports for riverbed cultivation.

4.5. Limitations

This study has several limitations. First, the cross-sectional design precludes causal inferences between agricultural tasks and musculoskeletal outcomes. Second, purposive sampling and baseline sociodemographic differences among the cohorts may introduce selection bias and limit generalizability. Third, the non-farming control group was relatively small (n = 35) due to recruitment challenges in rural areas. While major variables were assessed, unmeasured lifestyle factors could still act as confounders. Fourth, the reliance on self-reported questionnaires for assessing symptoms and operational durations introduces potential recall bias. Finally, the healthy worker effect may lead to an underestimation of true MSD prevalence, as severely injured individuals may have already left the agricultural workforce.
Future research should utilize prospective longitudinal designs to establish temporal relationships. Importantly, without objective field instrumentation, our interpretations of internal biomechanical mechanisms remain hypothetical. Subsequent studies should incorporate wearable inertial measurement units (IMUs) or surface electromyography (sEMG) to directly quantify joint kinematics and muscle activation during agricultural tasks on yielding terrain.

5. Conclusions

This study identified crop-specific biomechanical risks and musculoskeletal disorder (MSD) profiles among Taiwanese watermelon farmers. Our findings suggest a lower-limb dominant discomfort pattern—specifically localized in the hips and thighs—that differs from the upper-extremity issues in canopy-based orchards (pears) and the spinal strain associated with static stooping (pineapples). This localized symptom profile may be associated with the combined effects of prolonged deep squatting, dynamic heavy lifting, and postural stabilization on yielding sandy riverbeds. Furthermore, qualitative data indicated that low PPE adherence is primarily related to environmental incompatibility (sand accumulation and thermal stress) rather than a lack of safety awareness. These insights highlight the need for agricultural health policies to shift from generic guidelines to targeted, crop-specific frameworks. Future research should utilize prospective longitudinal designs and objective field instrumentation to evaluate the feasibility of breathable, sand-resistant ergonomic supports tailored to this specific environment.

Author Contributions

Conceptualization, S.Y. and C.-Y.C.; methodology, S.Y. and C.-J.C.; software, C.-Y.W.; validation, Y.-F.H. and H.-C.H.; investigation, S.Y., C.-Y.C. and C.-Y.W.; resources, K.-C.L.; data curation, S.Y. and C.-Y.C.; writing—original draft preparation, S.Y. and C.-Y.C.; writing—review and editing, S.Y., C.-J.C. and C.-Y.C.; funding acquisition, S.Y. and K.-C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Institute of Labor, Occupational Safety and Health, Ministry of Labor, grant number 1120049.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Governance Framework for Human Research Ethics of National Cheng Kung University (Protocol Code: 112147, approved on 24 April 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support this finding in this study are available from the corresponding authors upon reasonable request, subject to applicable ethical and legal considerations.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Demographic characteristics of the study population.
Table 1. Demographic characteristics of the study population.
CharacteristicsWatermelon Farmers (n = 60)Pear Farmers (n = 60)Pineapple Farmers (n = 63)Non-Farmers (n = 35)p-Value a
Age (years)56.1 ± 16.058.2 ± 13.645.7 ± 13.854.5 ± 13.5<0.001 ***
Gender, n (%)<0.001 ***
Male49 (81.7%)41 (68.3%)32 (50.8%)17 (48.6%)
Female11 (18.3%)19 (31.7%)31 (49.2%)18 (51.4%)
BMI Status, n (%)0.089
Underweight (<18.5)1 (1.7%)5 (8.3%)2 (3.2%)5 (14.3%)
Normal (18.5–24)17 (28.3%)23 (38.3%)27 (42.9%)11 (31.4%)
Overweight (24–27)19 (31.7%)10 (16.7%)18 (28.6%)16 (45.7%)
Obese (≥27)23 (38.3%)22 (36.7%)16 (25.4%)3 (8.6%)
Education, n (%)0.123
Primary or below12 (20.0%)8 (13.3%)5 (7.9%)2 (5.7%)
Junior high13 (21.7%)11 (18.3%)14 (22.2%)4 (11.4%)
Senior high20 (33.3%)24 (40.0%)29 (46.0%)11 (31.4%)
College or above15 (25.0%)17 (28.3%)15 (23.8%)18 (51.4%)
Lifestyle Habits, n (%)
Current Smoking8 (13.3%)15 (25.0%)20 (31.7%)5 (14.3%)0.085
Alcohol Consumption14 (23.3%)20 (33.3%)15 (23.8%)5 (14.3%)0.352
Betel Nut Chewing5 (8.3%)3 (5.0%)12 (19.0%)0 (0.0%)0.015 *
Work Characteristics
Farming seniority (years)25.8 ± 17.026.8 ± 15.316.6 ± 10.3N/A<0.001 ***
Daily working hours7.7 ± 2.27.1 ± 1.86.4 ± 1.4N/A0.082
Monthly Income, n (%)0.011 *
Low (<NT$20,000)10 (16.7%)12 (20.0%)9 (15.0%)0 (0.0%)
Medium (NT$20,000–60,000)45 (75.0%)42 (70.0%)44 (73.3%)20 (66.7%)
High (≥NT$60,000)5 (8.3%)6 (10.0%)7 (11.7%)10 (33.3%)
Operational Characteristics
Farm Area (hectares)5.6 ± 2.11.5 ± 0.56.8 ± 2.8N/A<0.001 **
Mechanization Use (hours/day)2.2 ± 0.41.2 ± 0.52.5 ± 0.7N/A<0.001 **
Leisure Physical Activity, n (%)0.003 *
Inactive24 (40.0%)29 (48.3%)34 (54.0%)7 (20.0%)
Occasional23 (38.3%)20 (33.3%)17 (27.0%)10 (28.6%)
Regular13 (21.7%)11 (18.3%)12 (19.0%)18 (51.4%)
History of Previous Injuries (Musculoskeletal Disorders), n (%)<0.001 **
Yes45 (75.0%)38 (63.3%)50 (79.4%)10 (28.6%)
No15 (25.0%)22 (36.7%)13 (20.6%)25 (71.4%)
Note: Data are presented as mean ± standard deviation (SD) for continuous variables and as frequency (percentage) for categorical variables. N/A: not applicable. a p-values were calculated using one-way ANOVA for continuous variables (age, farming seniority, daily working hours, farm area, and mechanization use) and the chi-square test for categorical variables across the four study groups. * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 2. Comparison of daily ergonomic risk exposure duration (hours/day) among the three farming groups.
Table 2. Comparison of daily ergonomic risk exposure duration (hours/day) among the three farming groups.
Ergonomic Risk FactorWatermelon Farmers (n = 60)Pear Farmers (n = 60)Pineapple Farmers (n = 63)p-Value aPost-Hoc Analysis b
Prolonged Fixed Posture3.20 ± 2.534.47 ± 2.424.33 ± 2.490.010 **Pear > Watermelon
Pineapple > Watermelon
Uncomfortable Posture2.00 ± 2.191.48 ± 1.892.68 ± 2.630.013 **Pineapple > Pear
Heavy Lifting1.83 ± 1.662.13 ± 2.112.13 ± 2.290.662-
Repetitive Motion1.78 ± 2.051.61 ± 1.902.30 ± 2.550.193-
Data are presented as mean ± standard deviation (hours/day); a one-way analysis of variance (ANOVA); b Tukey’s HSD post hoc test. * p < 0.05, ** p < 0.01.
Table 3. Prevalence of musculoskeletal disorders (MSDs) in different body regions across the four groups.
Table 3. Prevalence of musculoskeletal disorders (MSDs) in different body regions across the four groups.
Body RegionWatermelon Farmers (n = 60)Pear Farmers (n = 60)Pineapple
Farmers (n = 63)		Non-Farmers (n = 35)p-Value aPost Hoc Analysis b
Upper Extremities
Neck16 (26.7%)13 (21.3%)17 (27.0%)8 (22.9%)0.883-
Shoulders7 (11.7%)26 (42.6%)11 (17.5%)12 (34.3%)<0.001 ***Pear > Watermelon
Pear > Pineapple
Upper Back9 (15.0%)24 (39.3%)12 (19.1%)2 (5.7%)<0.001 ***Pear > Watermelon
Pear > Non-farmers
Elbows17 (28.3%)15 (24.6%)13 (20.6%)1 (2.9%)0.024 *Watermelon > Non-farmers
Pear > Non-farmers
Wrists/Hands10 (16.7%)17 (27.9%)19 (30.2%)3 (8.6%)0.039 *Pineapple > Non-farmers
Pear > Non-farmers
Lower Extremities & Back
Lower Back19 (31.7%)25 (41.0%)15 (23.8%)6 (17.1%)0.048 *Pear > Non-farmers
Pear > Pineapple
Hips/Thighs22 (36.7%)8 (13.1%)7 (11.1%)4 (11.4%)<0.001 ***Watermelon > Pear
Watermelon > Pineapple
Knees24 (40.0%)18 (29.5%)23 (36.5%)8 (22.9%)0.321-
Ankles/Feet19 (31.7%)10 (16.4%)15 (23.8%)1 (2.9%)0.007 **Watermelon > Non-farmers
Pineapple > Non-farmers
a Chi-square test or Fisher’s exact test; b Post hoc comparisons were performed using Fisher’s exact test; * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 4. Pearson correlation matrix of potential risk factors and musculoskeletal disorders (MSDs) among farmers.
Table 4. Pearson correlation matrix of potential risk factors and musculoskeletal disorders (MSDs) among farmers.
FactorShoulderUpper BackElbowHand/WristHip/ThighKnee
Demographics
Age−0.010.06−0.07−0.15 *0.03−0.19 **
Gender (Male)0.03−0.030.01−0.020.030.04
BMI−0.11−0.08−0.13−0.06−0.03−0.1
Farming Seniority−0.05−0.050.010.060.030.16 *
Work Characteristics
Farm Area0.03−0.080.03−0.01−0.02−0.07
Weekly Working Days−0.08−0.1−0.14−0.05−0.01−0.02
Daily Working Hours0.030.10.0100.19 *0.08
Daily Sun Exposure0.030.04−0.010.020.17 *0.07
PPE Usage
Hat/Cap0.0900.140.16 *−0.010.03
Eye Protection−0.050.010.060.040.040.00
Knee Pads−0.060.080.02−0.08−0.110.06
Back Support0.060.020.120.05−0.040.04
Ergonomic Exposures
Prolonged Fixed Posture0.130.110.040.120.030.09
Uncomfortable Posture0.090.15 *0.090.22 **0.28 **0.12
Heavy Lifting0.04−0.010.070.090.15 *0.11
Vibration Tools0.0900.13−0.010.070.00
Repetitive Motion0.120.010.17 *0.070.140.20 **
Values represent Pearson correlation coefficients (r); PPE: personal protective equipment; * p < 0.05, ** p < 0.01.
Table 5. Multivariate logistic regression analysis of independent risk factors for musculoskeletal disorders.
Table 5. Multivariate logistic regression analysis of independent risk factors for musculoskeletal disorders.
VariableShoulder Disorders aOR
(95% CI)		Upper Back Disorders aOR
(95% CI)		Hip/Thigh Disorders aOR
(95% CI)
Group (Ref: Pineapple)
Watermelon0.70 (0.30–1.67)1.81 (0.67–4.89)0.91 (0.26–3.27)
Pear1.17 (0.52–2.61)2.81 (1.10–7.17) *0.88 (0.26–3.01)
Demographics
Age1.00 (0.98–1.02)1.02 (0.99–1.04)1.00 (0.97–1.03)
Gender (Male)1.07 (0.54–2.11)1.14 (0.53–2.47)0.75 (0.29–1.94)
BMI0.95 (0.89–1.03)0.95 (0.87–1.03)0.99 (0.91–1.08)
Work Characteristics
Daily Work Hours1.07 (0.87–1.31)1.08 (0.86–1.35)1.38 (1.00–1.91) *
Ergonomic Factors
Uncomfortable Posture a1.08 (0.94–1.23)1.17 (1.01–1.36) *1.33 (1.11–1.59) **
Model Significancep < 0.001 ***p = 0.047 *p < 0.001 ***
aOR = adjusted odds ratio; CI = confidence interval; a defined as average daily hours adopting uncomfortable postures; * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 6. Prevalence of personal protective equipment (PPE) usage among the three farming groups.
Table 6. Prevalence of personal protective equipment (PPE) usage among the three farming groups.
PPE ItemWatermelon Farmers (n = 60)Pear Farmers (n = 60)Pineapple Farmers (n = 63)p-Value a
Hat/Cap49 (81.7%)51 (85.0%)58 (92.1%)0.229
Eye Protection11 (18.3%)17 (28.3%)13 (20.6%)0.387
Knee Pads3 (5.0%)6 (10.0%)3 (4.8%)0.421
Back Support4 (6.7%)7 (11.7%)8 (12.7%)0.507
a Chi-square test.
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MDPI and ACS Style

Yang, S.; Chuang, C.-Y.; Lee, K.-C.; Huang, H.-C.; Hsu, Y.-F.; Wang, C.-Y.; Chen, C.-J. Occupation-Related Musculoskeletal Disorders Among Watermelon Farmers in Taiwan: A Cross-Sectional Study. Occup. Health 2026, 1, 27. https://doi.org/10.3390/occuphealth1030027

AMA Style

Yang S, Chuang C-Y, Lee K-C, Huang H-C, Hsu Y-F, Wang C-Y, Chen C-J. Occupation-Related Musculoskeletal Disorders Among Watermelon Farmers in Taiwan: A Cross-Sectional Study. Occupational Health. 2026; 1(3):27. https://doi.org/10.3390/occuphealth1030027

Chicago/Turabian Style

Yang, Shinhao, Chi-Yu Chuang, Kun-Che Lee, Hsiao-Chien Huang, Ying-Fang Hsu, Chun-Yao Wang, and Chiou-Jong Chen. 2026. "Occupation-Related Musculoskeletal Disorders Among Watermelon Farmers in Taiwan: A Cross-Sectional Study" Occupational Health 1, no. 3: 27. https://doi.org/10.3390/occuphealth1030027

APA Style

Yang, S., Chuang, C.-Y., Lee, K.-C., Huang, H.-C., Hsu, Y.-F., Wang, C.-Y., & Chen, C.-J. (2026). Occupation-Related Musculoskeletal Disorders Among Watermelon Farmers in Taiwan: A Cross-Sectional Study. Occupational Health, 1(3), 27. https://doi.org/10.3390/occuphealth1030027

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