journal article·2025-06-27Open access

Postural Variability in Sitting: Comparing Comfortable, Habitual, and Correct Strategies Across Chairs

Yi-Lang Chen, Li-Peng Zhang

Applied Sciences· Volume 15· Issue 13

Abstract

Background/Objectives: Prolonged sitting is linked to musculoskeletal discomfort, yet optimal sitting posture remains poorly defined, and the consistency with which individuals reproduce specific sitting strategies is unclear. This study examined postural variability across three sitting strategies—comfortable, habitual, and correct—using three common chair types: a stool, computer chair, and ergonomic chair. Methods: Thirty healthy young adults (fifteen men, fifteen women) participated. Global sagittal joint angles—head inclination (HI), trunk angle (TA), and knee angle (KA)—were measured using a motion analysis system across five repetitions per condition. Results: The chair type significantly influenced HI and TA (p < 0.001), with ergonomic chairs encouraging more upright trunk postures. The sitting strategy significantly affected TA and KA (p < 0.01), with comfortable sitting associated with more extended angles. Women exhibited greater TA (114.8° vs. 109.0°, p < 0.001) and lower within-subject variability. Substantial postural variability was observed across all conditions, with mean ranges exceeding minimum detectable change thresholds for HI (10.3°), TA (6.9°), and KA (11.3°). Notably, correct sitting reduced KA variability compared to other strategies (p < 0.01). Conclusions: These findings highlight the individualized and variable nature of sitting posture, even under controlled instructions. The results question the reliability of memory-based seat adjustments and emphasize the need for dynamic, user-centered ergonomic design and personalized clinical guidance to support musculoskeletal health.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Ergonomics and Musculoskeletal Disorders · Color perception and design · Effects of Vibration on Health

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei 243303, Taiwan

Cheong Tat Technology, Tainan 710006, Taiwan

Funding

National Science and Technology Council (NSTC)

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