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Wearable Biosensors to Monitor Workload in Dancers: A Systematic Review.
Kelley R Wiese1, Jatin P Ambegaonkar1, Joel Martin1
1Sports Medicine Assessment, Research, and Testing (SMART) Laboratory, George Mason University, Manassas, VA, USA.
Summary
This systematic review found heart rate monitors and accelerometers are common wearable biosensors for assessing dancer workload. Most studies focused on internal workload, primarily in high-level female ballet dancers over short data collection periods.
Area of Science:
- Sports Science
- Biomechanics
- Human Movement Analysis
Background:
- Dancers are considered aesthetic athletes with high physical demands.
- Wearable biosensors are crucial for monitoring athlete workload, optimizing training, and preventing injuries.
- Limited research exists on wearable biosensor applications for dancer workload assessment.
Purpose of the Study:
- To systematically review the utilization of wearable biosensors for monitoring workload in dancers.
- To identify trends in wearable biosensor use for dancer workload assessment.
Main Methods:
- Systematic literature search following PRISMA guidelines across 8 databases.
- Inclusion criteria: current dancers, workload assessment via wearable biosensors during dance activity.
- Data extraction on dancer characteristics and methods; risk of bias assessment using JBI checklists.
Main Results:
- 35 studies were included, primarily using heart rate (HR) monitors (25) and accelerometers (12).
- Most studies (69%) assessed objective workload, and 66% assessed internal workload, mainly using HR.
- High-level female ballet dancers were predominantly studied, with data collection often limited to 1-3 days.
Conclusions:
- Heart rate monitors and accelerometers are the leading wearable biosensors for quantifying dancer workload.
- Current research predominantly focuses on internal workload using HR, with limited exploration of other biosensor data.
- Future research should consider diverse dancer populations and longer data collection periods for comprehensive workload analysis.
