Lumbar Lordosis and Transversus Abdominis Performance Among Sports, Cruiser, and Commuter Motorcycle Riders: A Cross-Sectional Study
Description
Background: Motorcycle designs require different riding postures that may influence lumbar spinal alignment and trunk muscle performance. Evidence comparing these outcomes across motorcycle types remains limited. Objective: To compare lumbar lordosis and transversus abdominis performance among sports, cruiser, and commuter motorcycle riders and examine their associations with motorcycle type and riding time. Methods: This cross-sectional observational study included 75 male riders aged 20–28 years, with 25 participants in each motorcycle group. Lumbar lordosis was measured using the Flexicurve method, and transversus abdominis performance was assessed indirectly using a Pressure Biofeedback Unit (PBU). Between-group differences were examined using one-way analysis of variance and Bonferroni post hoc tests. Regression analysis evaluated the combined contribution of motorcycle type and average riding time. Results: Lumbar lordosis differed significantly among groups (F = 29.881, p < 0.001, η² = 0.454), with the highest mean in sports riders (44.44 ± 8.61°), followed by commuter (38.56 ± 11.11°) and cruiser riders (25.75 ± 5.62°). Significant pairwise differences occurred between cruiser and sports riders and between cruiser and commuter riders. PBU values also differed overall (F = 5.237, p = 0.008), with a significant pairwise difference only between cruiser and commuter riders. Motorcycle type and riding time explained 52.4% of the variance in lumbar lordosis, compared with 13.9% in PBU values; the latter model was not statistically significant. Conclusion: Motorcycle type and riding time were associated with lumbar alignment, while associations with PBU-measured muscle performance were less consistent. These findings highlight the relevance of motorcycle ergonomics to rider spinal posture.
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Steps to reproduce
1. Recruit eligible male motorcycle riders aged 20–28 years according to the predefined inclusion and exclusion criteria. 2. Classify participants according to their usual motorcycle category: sports, cruiser, or commuter, with 25 participants in each group. 3. Record participant characteristics and riding exposure variables, including age, motorcycle category, riding duration, and riding frequency. 4. Measure lumbar lordosis using the Flexicurve ruler. Identify the L1 to L5/S1 landmarks by palpation, mould the ruler to the lumbar contour with the participant standing in a relaxed position, trace the contour onto graph paper, and calculate the lumbar lordosis angle. 5. Assess transversus abdominis (TrA) performance using a Pressure Biofeedback Unit (PBU). Position the participant prone, place the pressure cuff beneath the lumbar region, inflate it to 70 mmHg, and perform the abdominal drawing-in manoeuvre while maintaining normal breathing and keeping the lumbar spine and pelvis still. Record the resulting pressure change. 6. Classify lumbar lordosis according to the study protocol: 30°–45° as normal, below 30° as hypolordosis, and above 45° as hyperlordosis. 7. Classify PBU responses according to the study protocol: a pressure drop of approximately 4–10 mmHg as good TrA activation, less than 4 mmHg as reduced activation, no pressure change as poor or absent activation, and a pressure increase greater than 10 mmHg as suggestive of compensatory global abdominal muscle activation. 8. Enter the de-identified participant-level data into the dataset. No personally identifiable information is included. 9. Analyse continuous variables using mean ± standard deviation. Compare the three motorcycle groups using one-way ANOVA followed by Bonferroni post-hoc testing. 10. Analyse categorical variables using the chi-square test or Fisher's exact test, as appropriate. Consider p < 0.05 statistically significant. 11. Use the de-identified dataset deposited in this repository together with the study methodology described in the associated article to reproduce the reported analyses and results.
Institutions
- Acharya Institute of TechnologyKarnataka, Bengaluru