Physical and Mental Workload and Sleep in Collegiate Dancers

Dancers are aesthetic athletes that sustain significant physical and mental workloads. Collegiate dancers must balance the intense workloads of their training with academic responsibilities and environmental challenges of a college student, similar to collegiate athletes. Despite these demands, dancers often receive less recognition and support for workload management. Sleep is critical for recovery, wellness, and performance, but there is a prevalent culture of sleep deprivation in college settings. While workload monitoring is commonly used in athletics to track exertion, mitigate fatigue, and reduce injury risk, it is not widely implemented in dance. Wearable biosensors offer a practical approach to measuring workload and sleep, yet there is limited longitudinal research quantifying the combined physical and mental demands of collegiate contemporary dancers while also assessing their sleep habits. Therefore, the purpose of this dissertation was to use a 3-paper manuscript format to 1) systematically review how wearable biosensors are used to monitor workload in dancers, 2) provide a week-long snapshot of collegiate contemporary dancers' physical and mental workload alongside their sleep habits, and 3) longitudinally assess how these variables change during 4 weeks of an academic year.In study 1, we systematically reviewed study methods and participant characteristics in 35 studies that assessed workload using wearable biosensors for current dancers during dance activity. Study 2 (n=35 dance majors) and study 3 (n=40 dance majors) used observational cohort designs. ActiGraph GTX9 or GTX3 accelerometers (ActiGraph, Pensacola, FL) objectively quantified physical workload (moderate to vigorous physical activity, MVPA; min) and total sleep time (TST; min). Mental workload was quantified using the Mental and Physical State and Trait Energy and Fatigue Scale and subjective sleep was quantified using the PROMIS Sleep-Related Impairment (SRI) and Sleep Disturbance (SD) Short Forms. Hierarchical linear modeling (HLM) and a Location-Scale Model was used in study 2 to investigate growth patterns of weekly physical activity and between- and within-dancer variability in physical workload. In study 3, repeated-measures HLM analyzed changes in workload and sleep. Study 1 identified that 1) heart rate monitors, followed by accelerometers, are the most common wearable biosensors used, 2) most studies only evaluated objective physiological workload, 3) data was primarily collected over 1-3 days, 4) high-level female ballet dancers were mostly assessed. Study 2 showed the dancers 1) engaged in more than 7 hours/day of MVPA but slept less than 7 hours/night, 2) self-reported average levels of fatigue and energy with minimal perceived sleep disruptions and sleep-related impairments, 3) MVPA varied between and within dancers across 1 week, but variance was not predicted by academic year, subjective sleep or mental workload variables. Study 3 identified 1) dancers had consistently high physical workloads but insufficient sleep durations across the academic year, 2) the dancers reported increasing fatigue and decreasing energy levels but reported sleep disturbances and sleep-related impairments did not meaningfully change. This dissertation is among the first to longitudinally assess physical and mental workload and sleep in collegiate contemporary dancers. Collectively, the primary findings indicate that the participating dancers sustained high physical and mental workloads but were not getting enough sleep for recovery. This imbalance may lead to negative physical and mental health outcomes. Further, the results highlight the usefulness of workload and sleep monitoring in collegiate dance programs to adapt training accordingly, identify dancers at risk of poor sleep habits and prioritize sleep for recovery.

Author

• Wiese, Kelley R

• Dance
• Sleep
• Wearable Biosensors
• Workload

Identifier

hdl:1920/14803

OAI identifier oai:identifier

oai:MARS:1920/14803