ArticleScientific reports2025
Long term gait postural characteristics of children with general foot pain using smartphone connected wearable sensors.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic Systems for Monitoring Pediatric Gait Biomechanical Parameters: A Systematic Review of Embedded Technologies and Human-Machine Interfaces.Sensors (Basel, Switzerland) · 2026Pooled it
- Non-Invasive Wearables in Pediatric Healthcare: A Comprehensive Review of Uses and Implications.Children (Basel, Switzerland) · 2025Review
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Authors and funding
16 authors.
Funding
Abstract
Whether children's general foot pain related to specific foot posture remains unknown. The aim of the study was to determine long-term dynamic foot posture characteristics in children with general foot pain using smartphone-connected wearable sensors. Over a 30-day period, 94 children (37 girls and 57 boys, mean age 9.3 years) completed the study. Children were grouped according to the recent history of foot pain during walking. Static rearfoot alignment was digitally measured. Long-term gait posture was collected using wearable sensors during 10 m of self-selected speed walking per day. There were 81 children in the asymptomatic group and 13 in the foot pain group. Static and gait parameters showed moderate to strong correlations between left and right, but only the left foot parameters showed group differences for foot pain. Increased static rearfoot angle (RA) and decreased coronal movement at toe-off (TOCP) of the left foot were significantly associated with foot pain (p < 0.05). This study demonstrates IMU-based identification of gait characteristics in children with foot pain, revealing associations with increased static rearfoot angle and reduced dynamic propulsion posture at coronal plane. These findings provide a foundation for developing IMU-based algorithms to predict musculoskeletal disorders in children, enabling early detection and intervention strategies.
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