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bioRxiv · 10.1101/2024.12.02.626456

ROSLT: An Improved Raspberry-Pi Open-Source Live Voluntary-Wheel Running Tracker Method and Resource

Sanosa, P.·Marrow, J. P.·Malicki, A. R.·Brunt, K. R.·Simpson, J. A.
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Abstract

BackgroundExercise promotes health and has therapeutic effects on disease. Over time, the body improves its maximal exercise capacity through training adaptations such as an increase in VO2 max. In mice, voluntary wheel running allows for a natural setting to test spontaneous running behaviour under non-stressed conditions. There is a need to design sensitive animal-based assay that improves resolution for differentiating exercise performance from a regular cyclometer (which presents a single value from a summary of dynamic data collected over time) and offer circadian analyses. The purpose of this work is to examine the exercise behaviours of mice with a focus on circadian rhythm of running. We hypothesize the pi cyclometer (ROSLT) will mirror VDO M2.1 behaviors, enhancing circadian rhythm insights. MethodsUsing a hand-built cyclometer programmed through the Raspberry Pi computer, voluntary wheel running behaviours in CD-1 mice ([~]8-10 weeks) were recorded for 6 consecutive days. This features a Hall Effect sensor and neodymium magnets attached on the running wheels that will detect changes to wheel rotation, speed, acceleration, and distance (continuously) and publish the data to a server in real-time. To compare capabilities, running wheels will also be equipped with the VDO M2.1 WR Cycling Computer to track distance which will be manually recorded once a day. Accuracy from both devices were mechanically validated. ResultsThe main findings include that voluntary wheel running distance over 6 days produces inaccuracies by the VDO. The VDO showed fluctuations in distance over the last 3 days, while ROSLT showed consistent measurements. ConclusionThis comparison shows that ROSLT expands on the running activity of mice each day while maintaining accuracy and precision. This novel dynamic circadian cyclometer will advance our research abilities and can be used in differentiating exercise performance in applications such as doping.

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Sanosa, P., Marrow, J. P., Malicki, A. R., Brunt, K. R., Simpson, J. A.. 2024-12-05. ROSLT: An Improved Raspberry-Pi Open-Source Live Voluntary-Wheel Running Tracker Method and Resource. https://doi.org/10.1101/2024.12.02.626456

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