Questions from the training ground
Useful answers begin with the limits of the question. These are starting points for discussion, not individual training or medical prescriptions.
Can a wearable predict an individual injury?
No single wearable reading can reliably determine whether an individual injury will occur. Monitoring can inform a broader assessment of exposure, response and circumstances, but it does not replace clinical evaluation. A warning flag is a reason to investigate, not proof of an impending injury. See workload context.
Which sensor is best for professional athletes?
There is no universal best sensor. Choose a method with validation relevant to the movement, environment and decision you need to support. Outdoor running exposure, indoor court positioning and limb movement analysis create different requirements. Examine export access, operating constraints and staff capacity as well as the device itself. Start with signals in motion.
Can workload scores from different brands be compared?
Not by default. Definitions, placement, filters and algorithms can differ. Check the underlying methods before combining or comparing scores. A familiar metric name is not evidence of equivalence, and a shared unit does not guarantee identical processing. Preserve a clear boundary in your historical record when systems change.
Does a higher sampling frequency guarantee better accuracy?
No. Sampling frequency describes how often a system samples or reports data, not whether each value is accurate. Hardware, signal conditions and processing also matter. Some reported values may be interpolated or filtered. Look for validation against a suitable reference under conditions resembling your own use.
What should staff do when the athlete and dashboard disagree?
Check data quality, discuss the athlete’s experience and review session context. Escalate symptoms to qualified medical staff rather than allowing a score to overrule the athlete. Disagreement can reveal a useful missing detail: a changed drill role, a faulty signal or a demand the chosen metric does not capture.
How long should a baseline be?
There is no universal duration. Use repeated comparable observations and judge whether they represent normal variation for the athlete, measure and decision. A short, noisy history should support a cautious conclusion. Record protocol and equipment changes rather than assuming that a longer data history is automatically a better one. Read progress over seasons.
Can GPS track a session reliably indoors?
Satellite signals are generally unsuitable for reliable indoor sports tracking. A validated local positioning, optical or task-specific inertial system may be more appropriate. Check venue coverage and calibration. An inertial estimate is not automatically a substitute for measured position, and an indoor-capable label does not remove the need for task-specific validation.
Should a low recovery score cancel training?
Not automatically. Check the measurement, symptoms, recent work and planned session. A proprietary recovery score is not a medical clearance or diagnosis. The right response may be further discussion, a modified session, normal training or clinical assessment, depending on the circumstances. Persistent symptoms deserve professional attention regardless of the score.
Who should be allowed to see athlete data?
Access should be limited to people with a legitimate role and supported by clear governance, appropriate legal grounds and relevant safeguards. Athletes should understand the purpose and handling of their information. Avoid circulating identifiable health-related details simply because a platform makes sharing convenient. See the governance section in our practical workflow.
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