Wearable Biosensors to Monitor Workload in Dancers: A Systematic Review
Bibliographic record
Abstract
Introduction: Dancers are aesthetic athletes with high workloads similar to sport athletes. Wearable biosensors are used in athletics to assess workload and inform training decisions to optimize performance and reduce fatigue and injury risk. While workload monitoring methods in dancers have been systematically reviewed, limited research specifically examines using wearable biosensors for this purpose. Thus, this study aims to systematically review how wearable biosensors are used to monitor workload in dancers. Methods: Following PRISMA guidelines, 8 databases were searched by 2 authors. Articles were included if participants were current dancers, workload was assessed during dance activity by wearable biosensors, and published in English in a peer-reviewed journal. Dancer characteristics (age, sex, anthropometrics, years dancing, training level, dance style) and methods (sessions, variables, setting, biosensor) were extracted and synthesized in an Excel synthesis matrix. The Joanna Briggs Institute (JBI) Critical Appraisal Checklists were used to assess the risk of bias. Results: 35 of 119 potentially relevant studies were included. Heart rate (HR) monitors (25 studies) and accelerometers (12 studies) were primarily used. 24 studies (69%) examined only objective workload and 23 studies (66%) examined internal workload. The most common dependent variable was HR (25 studies). The duration of data collection ranged between 1 and 49 days, with 26% of studies (n = 9) using a single day. High-level (14 studies) female (74.7%; n = 1342) ballet dancers (45.7%; n = 16 studies) were most assessed. Risk of bias was fair-to-moderate across studies. Conclusions: This systematic review highlights 4 primary trends across previous literature assessing workload in dancers using wearable biosensors to inform future research. First, HR monitors, followed by accelerometers, are the most common wearable biosensors used to quantify workload in dancers. Second, most studies only evaluated objective physiological (internal) workload, primarily using HR variables. Third, data were primarily collected within a timeframe of 1 to 3 days. Finally, high-level female ballet dancers were predominantly assessed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".