Wearable analysis
Medical wearable data analysis in Munich
Wearable analysis places longer-term activity, heart-rate, sleep and recovery data in a health context. The aim is not to explain every fluctuation, but to distinguish robust patterns from measurement errors and normal variation.
At a glance
Key facts
- Consider data quality and changes of device
- Assess long-term patterns rather than isolated alerts
- Mark training, illness and daily events on the timeline
- Have medically concerning findings confirmed with targeted assessment
Your data
Context makes the curve understandable.
Travel, infections, alcohol, hard training blocks and shift work can visibly affect data. A short timeline of events is more useful than thousands of unexplained measurements and helps avoid overinterpreting normal responses.
Medical interpretation
Consumer devices are useful, but not diagnostically complete.
Optical sensors and algorithms have limitations, particularly during movement or with an irregular pulse. An unusual trend may prompt a targeted examination. Diagnosis and treatment should not be based solely on an app display.
Common questions
Answers to common questions.
How much data is needed for a useful analysis?
A consistent record over several weeks is usually more helpful than a few days. The appropriate duration depends on the question and measurement.
Should I stop training immediately after an alert?
Not every alert is medically relevant. Symptoms, repeated substantial abnormalities or a marked change in how you feel are reasons to seek medical assessment.
Author and medically responsiblePD Dr. med. Daniel P. BertholdSpecialist in Orthopaedics and Trauma SurgeryUpdated
Next step
Discuss performance testing and prevention
Describe the goal and current training or health context. The inquiry can then be matched to the appropriate performance or prevention offer.
