SqueakSpeed: A Low-Cost, Open-Source System for Tracking Voluntary Wheel Running in Rodents
Bibliographic record
Abstract
Background: Exercise 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 behavior 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 behaviors of mice with a focus on circadian rhythm of running. We hypothesize our newly developed pi cyclometer (SqueakSpeed) will mirror VDO M2.1 behaviors, with enhanced circadian rhythm insights. Methods: Using a hand-built cyclometer programmed through the Raspberry Pi computer, voluntary wheel running behaviors in CD-1 male 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 by using a DC motor with a speed controller. Results: The 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 ranging from ~4 km differences, while SqueakSpeed showed consistent measurements with a steady increase of about ~1 km each day. In a separate experiment, SqueakSpeed recorded ~3.7 km at ~0.1 m/s while VDO measured ~7.6 km at ~0.6 m/s for 24 hours in-vivo. When both devices were compared using a motor, an absolute value of 3.7 km was set before SqueakSpeed stopped recording in comparison to the VDO which stopped at ~4km. Conclusion: The findings indicate that the VDO exhibits measurement inaccuracies, particularly over extended periods of voluntary wheel running, with fluctuations in recorded distances. In contrast, SqueakSpeed provides more consistent and reliable measurements, demonstrating a steady and predictable increase in distance. Discrepancies between the two devices were also observed in both in-vivo and motor-driven experiments, further highlighting the VDO’s overestimations. These results suggest that SqueakSpeed may be a more accurate tool for assessing running distances and can be used in differentiating exercise performance in applications such as doping. The author(s) acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC). This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".