SqueakSpeed: A Low-Cost, Open-Source System for Tracking Voluntary Wheel Running in Rodents
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».