Martin Buchheit, Ben M. Simpson, Walter F. Schmidt, Robert J. Aughey, Rudy Soria, Robert A. Hunt, Laura A. Garvican-Lewis, David B. Pyne, Christopher J. Gore and Pitre C. Bourdon. Predicting sickness during a 2-week soccer camp at 3600 m (ISA3600). British J Sports Med 2014, In press.
What is known on this subject
- Living and training at high-altitude presents a physiological challenge for native sea-level athletes, with depressed immune function, altered autonomic function, acute mountain sickness and sleep disturbance often reported.
- Psychometric and physiological measures, such as questionnaires or resting HR, are used as indicators of general acclimatisation and tolerance to altitude.
What this study adds
- A >4% increased HR during submaximal exercise, in response to a moderate increase in perceived training load the day before, may be predictive of sickness the following day.
- All other variables examined including resting HR, HR variability or psychometric questionnaires don’t show consistent changes before sickness
Objectives. To examine the time-course of changes in wellness and health status markers before and after episodes of sickness in young soccer players during a high-altitude training camp (La Paz, 3600m). Methods. Wellness and fatigue were assessed daily upon awakening using specifically-designed questionnaires and resting measures of heart rate (HR) and HR variability. The rating of perceived exertion and HR responses to a submaximal run (9 km·h-1)were also collected during each training session. Players who missed the morning screening for at least two consecutive days were considered as sick. Results. Four players met the inclusion criteria. With the exception of submaximal exercise HR, which showed an almost certain and large increase before the day of sickness (+4%; 90% confidence limits 3,6), there was no clear change in any of the other psychometric or physiological variables. There was a very likely moderate increase (+79%, 22,64) in self-reported training load the day before the HR increase in sick players (4 of the 4 players, 100%). In contrast, training load was likely and slightly decreased (-24%, -78,-11) in players who also showed an increased HR but remained healthy. Conclusion. A >4% increased HR during submaximal exercise in response to a moderate increase in perceived training load the previous day may be an indicator of sickness the next day. All other variables, i.e., resting HR, HR variability and psychometric questionnaires may be less powerful at predicting sickness.
The old website in French is down after 8 years of activity
[le site en Francais vient de fermer apres 8 année d’activité]
On this new webiste you’ll find all materials related to my research, e.g., papers, videos, slides, posters. Don’t hesitate to comment and share… For any reprint, please contact me!
[Sur ce nouveau site vous trouverez différents document lies à mon activité de recherche, par ex des vidéos, des présentations, des posters. N’hésitez pas à commenter les posts et à partager… Pour toute demande d’article ou autre, emailez-moi !]
Aren’t we losing our time? GPS-derived accelerations are not as good as we wished! 😦
Here is the sled used for the tests...
Monitoring Accelerations With GPS in Football: Time to Slow Down?
Physiology Unit, Football Performance and Science Department, ASPIRE Academy for Sports Excellence, Doha, Qatar.
The aim of the present study was to 1) examine the magnitude of between-GPS model differences in commonly reported running-based measures in football, 2) examine between-unit variability and 3) assess the effect of software updates on these measures. Fifty identical brand GPS units (15 SPI-proX and 35 SPI-proX2, 15 Hz, GPSports, Canberra, Australia) were attached to a custom-made plastic sled towed by a player performing simulated match running activities. GPS data collected during training sessions over 4 weeks from 4 professional football players (n = 53 files) were also analyzed before and after 2 manufacturer-supplied software updates. There were substantial differences between the different models (e.g., standardized difference for the number of acceleration >4 m.s-2 = 2.1; 90% confidence limits (1.4, 2.7), with 100% chance of a true difference). Between-unit variations ranged from 1% (maximal speed) to 56% (number of deceleration >4 m.s-2). Some GPS units measured 2 to 6 times more acceleration/deceleration occurrences than others. Software updates did not substantially affect the distance covered at different speeds or peak speed reached, but one of the updates led to large and small decreases in the occurrence of accelerations (-1.24;-1.32,-1.15) and decelerations (-0.45; -0.48,-0.41), respectively. Practitioners are advised to apply care when comparing data collected with different models or units, or when updating their software. The metrics of accelerations and decelerations show the most variability in GPS monitoring and must be interpreted cautiously.