We have developed a Hublink-enabled running wheel for mice.
Our wheel is self-contained and will run for two months on a single charge. Compatible with the hublink.cloud platform, our wheel will log wheel revolution-per-minute onto a micro-SD card and automatically sync with your Hublink account for visualization and data retrieval.
Want to learn more?
Let’s take a look at why running wheels are perhaps the most meaningful behavioral metric you can easily add to your lab.
Introduction
A battery-powered running wheel data logger for mice offers an automated way to record each wheel revolutions or rate with a timestamp. Such devices enable continuous home-cage monitoring of voluntary running activity without cables or manual data collection. Voluntary wheel-running is a widely used behavioral measure in rodent neuroscience research, providing insights into locomotor activity, motivation, and physiology. Unlike forced exercise paradigms (e.g. treadmills that require aversive stimuli), a running wheel allows mice to freely exercise, making it a more naturalistic and less stressful assessment of activity and endurance.
Several digital running wheels have been published, each having different trade-offs in terms of size, cost, and data availability.
In addition to these, MedAssociates also offers a Low-Profile Wireless Running Wheel requiring the use of their wireless hub (Windows-compatible only).
Next, we explore key research areas where timestamped wheel-running data is critical, how such data yields behavioral or physiological insights, and what advantages and new experiments are enabled by these self-contained wireless systems.
Behavioral Neuroscience and Exercise Effects
In behavioral neuroscience, running wheels are used to assess voluntary exercise, motivation, and physical performance. Metrics like total distance run, running speed, and endurance indicate an animal’s fitness or willingness to engage in exercise. For example, daily distance or time spent running can serve as an index of baseline activity levels, which vary by strain, age, sex, and individual – factors that must be accounted for in experimental design. Importantly, wheel running is a rewarding behavior that activates brain reward pathways similarly to natural rewards. As a result, giving mice access to a wheel is not only a readout of activity but can also be an experimental intervention (simulating exercise training).
Studies have shown that voluntary running has beneficial effects on the brain: for instance, it increases hippocampal neurogenesis and produces antidepressant-like outcomes in rodent models of depression (with parallels in human depression). Such findings indicate that logged wheel-running data can be used to correlate exercise amounts/patterns with neurobiological changes (e.g. neurogenesis, neurotransmitter levels) and behavioral outcomes (e.g. reduced anxiety or depression-like behavior). In one study, mice deficient in a muscle-related gene (klotho) showed a “sporadic running pattern” with repeated bouts of exhaustion – they ran at normal speeds but for significantly less total time than controls. This demonstrates how wheel data can detect subtle deficits in endurance or motivation, serving as a measure of muscle or neural performance in various genetic backgrounds.
Timestamped revolution data provides fine-grained detail on when and how mice run, yielding insights beyond simple distance totals. Mice typically run in bouts (spurts) of activity separated by rest periods. With time-stamped logs, researchers can quantify bout frequency, bout duration, and inter-bout intervals, which reflect motivation and fatigue. For example, a high-resolution logger can capture how many running bouts occur per day and how long each bout lasts. This was applied in a Parkinson’s disease (PD) model, where 6-OHDA-lesioned rats exhibited fewer running bouts and shorter total distance than controls, and even when they did run, their peak running speed was lower and it took them longer to accelerate to that peak speed. These detailed metrics, made possible by timestamped data, clearly indicated motor deficits in the PD model, leading researchers to conclude that wheel-running behavior is a useful tool to quantify subtle motor impairments. In healthy mice, similarly, monitoring the distribution of bouts can indicate stamina or fatigue – e.g. a mouse that runs in one long bout vs. many short bouts may have different endurance or motivation levels.
Another behavioral application is using the wheel as an exercise intervention. Here, the wireless logger tracks how much each mouse voluntarily runs when given access to a wheel (versus a control group with a locked wheel). This design has been used to study the effects of exercise on disease models. For instance, long-term voluntary wheel running (over weeks to months) in a PD transgenic mouse model upregulated neuroprotective factors (like DJ-1) and prevented age-related declines in motor and cognitive performance. In other words, the running wheel served as both a therapeutic tool and an outcome measure, with the logged data confirming the animals’ exercise levels and timing. In general, behavioral neuroscience studies rely on wheel data to link physical activity patterns with brain health, cognitive function, and mood. Timestamped data adds value by revealing when the animals prefer to run (active phase vs. inactive phase), how consistently they maintain activity, and how interventions (stress, drugs, training paradigms) alter these patterns in time.
Neurodegenerative Disease Models
Neurodegenerative disease research often employs wheel-running to evaluate motor function and motivation as diseases progress or in response to treatments. Many neurodegenerative models (e.g. Parkinson’s, Huntington’s, Alzheimer’s disease models) show changes in spontaneous activity. The running wheel provides a non-invasive longitudinal assay of motor deficits: as symptoms worsen, mice typically run less or slower. In a unilateral 6-OHDA Parkinson’s model, as noted above, lesioned rats’ wheel activity was dramatically impaired relative to controls across multiple metrics. The timestamped revolution data was essential to detect not just that they ran less, but that their pattern of running had changed (fewer bouts, slower acceleration, etc.), indicating specific motor coordination and endurance issues. Because the wireless logger can record data continuously over weeks, researchers can track disease progression within the same animals and observe when deficits emerge or plateau. In the PD model, wheel-running tests were conducted up to 42 days post-lesion, and the deficits in bout number and speed persisted – information that a long-term logger could capture without daily handling of the animals.
Wheel data is also valuable for testing therapeutic strategies in neurodegeneration. For example, providing an exercise opportunity via a running wheel has been shown to ameliorate pathology in PD and other models. In a transgenic α-synuclein mouse model of Parkinson’s, mice with access to a wheel for several months had reduced toxic protein aggregation and improved motor and cognitive outcomes compared to sedentary counterparts. Here, the logged running data verified how much each mouse exercised, which could then be correlated with their improvements. Similarly, wheel-running has been used in Alzheimer’s models to see if exercise improves cognitive performance or reduces amyloid pathology, and in Huntington’s models to assess motor stamina. In all cases, having time-stamped logs means researchers can examine whether improvements are linked to certain patterns of running (e.g. running primarily at night vs. scattered across 24 hours, steady daily distance vs. progressive increase in distance, etc.). It also enables detection of early changes: for instance, a very mild motor deficit might first appear as a change in the timing of activity (perhaps the animal delays starting its running each evening due to bradykinesia or disorientation). Indeed, such delays in activity onset and fragmented running patterns have been observed in aging mice (a natural neurodegenerative model), as discussed in the next section.
Circadian Rhythm and Sleep Research
Circadian biology is one of the classic domains for wheel-running data. Nocturnal rodents like mice exhibit robust daily rhythms in wheel activity, making the running wheel a primary tool for tracking internal circadian clocks. A timestamped running wheel logger effectively acts as an actigraph, recording when the mouse is active or resting. Researchers analyze this data to determine activity onset times, activity offsets, total activity per circadian cycle, and rhythmic period length. For example, in a standard light/dark cycle, healthy young mice will confine most running to the dark phase and start running shortly after lights-off. If we continuously log revolutions, we can plot actograms (activity vs. time plots) that reveal the timing of running each day and how it shifts under different conditions. Using the high temporal resolution data, scientists can detect if an animal’s activity onset is drifting (indicative of its free-running period when no light cues are present) or how quickly it shifts when the light cycle is changed (indicating the adaptability of its circadian clock).
Timestamped wheel data has been critical in discovering and characterizing clock gene mutants and other circadian phenomena. As a case in point, studies on aging showed that older mice have notable alterations in their wheel-running rhythms: under a normal 12:12 light/dark cycle, older mice had a delayed and more variable activity onset each evening, and after a phase shift in the light schedule, they took longer to re-entrain (adjust) their running rhythm.
Continuous logging further revealed that old mice run less overall (fewer wheel revolutions per day) and that their activity is more fragmented into shorter bouts with longer rests, compared to younger adults. These fine details – increased fragmentation and day-to-day variability – were only quantifiable thanks to time-stamped data capturing every revolution. In constant darkness (no external cues), the same studies found that older mice had a significantly longer free-running period than young mice. All of these metrics (onset time, bout length, period length) are fundamental circadian parameters that a wireless wheel logger can provide non-invasively.
Beyond circadian rhythm research per se, wheel-running data serve as a proxy for sleep-wake cycles. When a mouse is not running (or otherwise active), it is often assumed to be resting or sleeping. By setting a threshold of wheel activity to define “wakefulness,” scientists use wheel logs to estimate sleep duration and fragmentation in home-cage conditions. This approach has been used to link genetic or pharmacological manipulations to changes in sleep-like behavior. For instance, if a drug causes sedation, the mouse might show prolonged periods with zero wheel revolutions during its normal active phase (suggesting extended rest). Conversely, a stimulating intervention might lead to wheel activity occurring at abnormal times (e.g. running during the usual rest/light phase, indicating disrupted sleep). Circadian pharmacology experiments also benefit from wheel loggers: researchers can administer drugs at specific circadian times and measure subsequent shifts in the timing or amount of running, thereby gauging the drug’s impact on the body clock or activity level.
Pharmacological and Drug Testing Applications
In pharmacological testing, voluntary wheel running provides a sensitive readout for a drug’s effects on locomotor activity, circadian timing, and neuromotor function. Timestamped wheel data can reveal both acute and long-term drug effects. For example, psychostimulant drugs (like amphetamines or cocaine) typically cause hyperactivity in open-field tests; with a wheel logger, one can quantify if these drugs also increase running speed or distance – or possibly disrupt the normal circadian pattern of running. Interestingly, not all drugs affect wheel running in the same way as general locomotion. It has been reported that selective serotonin reuptake inhibitors (SSRIs, a class of antidepressants) actually decrease wheel-running activity in mice, even though those same animals might show normal or increased movement in open-field tests. This suggests wheel running may engage specific motivation or reward circuits that SSRIs impact (e.g. reducing the drive to seek the wheel’s reward or causing lethargy that specifically curtails voluntary exercise). A wireless logger would capture such reductions in running, including when they occur (e.g. perhaps SSRIs cause more daytime inactivity). On the other hand, drugs that elevate dopamine signaling can alter running intensity – one study on mice bred for high voluntary running found that dopamine transporter blockers like Ritalin or cocaine reduced the high-intensity running bursts these mice normally display. This kind of fine observation (a reduction in peak running speed or intensity, rather than total abolition of movement) underscores the value of high-resolution wheel data in pharmacology. It enables researchers to detect nuanced drug effects: whether a compound causes animals to run at different times (phase shifts), at different speeds, or with different patterns (short frequent runs vs. long endurance runs).
Furthermore, wheel running can serve as a baseline and outcome measure in drug trials for neurological conditions. For instance, in testing a potential treatment for a motor disorder, an increase in daily wheel rotations or a shift toward a normal circadian pattern of running in treated mice would indicate improvement. In addiction research, giving rodents access to a wheel can modulate drug-seeking behavior – animals with the opportunity to run often show reduced drug self-administration or a blunted response to drugs. Thus, logging wheel activity can be an indirect way to measure a drug’s efficacy in reducing cravings or withdrawal symptoms (more running might imply the drug or the exercise is substituting for the drug reward). Because the logger timestamps every revolution, researchers can align drug dosing times with activity changes. For example, if a sedative is given at 1 PM, the device might record a sharp drop in wheel turns within the hour, quantifying the onset and duration of the drug’s sedative effect. If tolerance develops with repeated doses, the wheel data would show gradually less suppression of activity over days. In summary, pharmacological testing benefits from wireless wheel monitors by allowing continuous, real-time tracking of drug effects on behavior in the animal’s home cage, across circadian cycles, without the need for manual observation.
Advantages of Wireless Logging over Traditional Systems
Introducing a wireless, battery-operated wheel logger brings several key advantages over older or more cumbersome activity monitoring systems:
- Reduced Handling and Stress: With wireless data upload, researchers no longer need to physically handle the mouse or the wheel to retrieve data (such as removing the wheel or animal to download a counter’s readings). Traditional systems often required either tethered sensors or periodically checking mechanical counters, which could disturb the animals. In contrast, a logger that transmits data via a hub allows fully unobtrusive monitoring, improving animal welfare and yielding more natural behavior data. Mice can remain undisturbed in their home cages for weeks or months, which is crucial for experiments on chronic conditions, aging, or long-term drug effects. Continuous home-cage monitoring was historically challenging, but with a self-contained logger, it becomes feasible to track an animal’s activity 24/7 over very long periods.
- Long-Term Home-Cage Monitoring: Because the device is battery-powered and wire-free, it can be placed in the home cage indefinitely, logging every wheel revolution with a timestamp. This enables longitudinal studies that follow the same individuals over developmental stages or disease progression. For example, one can measure how running behavior changes as a neurodegenerative disease advances from pre-symptomatic to advanced stages, all without changing the apparatus. Long-term data also capture variability and fluctuations in activity that short tests might miss. Continuous logging ensures that researchers can calculate stable baselines and observe gradual trends or cyclic variations in activity.
- Increased Throughput and Scalability: Wireless loggers make it easier to scale up the number of subjects. Laboratories can equip dozens of standard cages with wheel sensors, all uploading data to a central hub (e.g. the Hublink platform) simultaneously. This contrasts with older setups that might require dedicated wiring or data ports for each cage, becoming unwieldy at scale. With a proprietary wireless platform, data from many cages can be collected in parallel, enabling high-throughput studies of activity. For instance, a drug screening study could monitor wheel running in 50 mice at once, automatically aggregating data, whereas manual or tethered approaches would require far more labor. The cost-effectiveness of new systems also contributes to throughput – versus thousands of dollars for some commercial systems. More subjects and continuous data improve statistical power and the ability to detect subtle effects (e.g. a slight increase in nocturnal activity after a mild treatment).
- High Temporal Resolution and Rich Data: By logging each revolution with a timestamp, the system provides much finer temporal resolution than older methods that might only record cumulative counts over hourly or daily bins. The 1/100th second resolution of some Arduino-based loggers demonstrates how precisely wheel events can be timed. This rich data stream unlocks advanced analyses: researchers can compute metrics like circadian period to within a few minutes, identify the exact times of day when activity peaks, or even analyze running speed (revolutions per minute) dynamically. High-res data also facilitate the detection of short bouts of activity that might be averaged out in coarse sampling. Essentially, the timestamped approach treats the data almost like a continuous waveform rather than a single daily value. This is especially beneficial for circadian analysis, where precise timing determines phase shifts or jet-lag adjustments, and for identifying sleep/wake cycles where brief awakenings (short runs) could indicate sleep fragmentation. Additionally, because the data are uploaded in real time, researchers can observe ongoing experiments and even intervene if needed (for example, if an animal stops running due to a health issue, it could be noticed quickly).
New Experimental Opportunities and Analyses
A self-contained wireless wheel logger not only improves existing experiments but also enables new research designs and analytical approaches that were impractical before:
- Integrative Monitoring of Behavior and Physiology: With a platform like Hublink, multiple wireless devices can potentially be synchronized. This means one could log wheel-running alongside other sensors – for example, temperature or heart rate sensors, EEG for sleep, or video tracking – all time-aligned. The Arduino-based Hublink devices can include add-ons (e.g. environmental sensors for temperature/humidity, light sensors, etc. are supported in the Hublink BEAM project). This opens the door to experiments correlating running activity with physiological signals in real time. For instance, researchers could examine whether a spike in running speed corresponds to a rise in body temperature or changes in brain wave activity. The wireless logger thus becomes part of an IoT-like network of biosensors in the home cage, enabling a more holistic view of the animal’s state.
- Uninterrupted Circadian Experiments: Chronobiology studies often require strictly constant conditions (constant darkness, constant temperature, etc.) for weeks to measure free-running rhythms. A battery-powered logger that stores data locally and/or transmits wirelessly means mice can be left in a sealed environment (e.g. light-tight box) without needing human entry to download data or tend to equipment. This preserves the integrity of circadian experiments (no light leaks or disturbances at odd hours). Moreover, novel lighting paradigms or feedback loops can be implemented: since the device uploads data live, one could program lights or other stimuli to trigger when the animal has run a certain amount or at a certain circadian phase, creating closed-loop experiments (e.g. delivering a light pulse only after the mouse’s usual activity onset to test phase-shifting effects). Such responsive experimental designs are more feasible with online data from wireless loggers.
- Group or Social Housing Studies: Traditionally, wheel monitoring requires single housing (one mouse per wheel) to attribute activity to the correct animal. While that is still necessary for precise data, wireless devices make it easier to introduce wheels into more complex housing scenarios. For example, in a social context study, one could place multiple wheels in a large enclosure with several mice, each wheel equipped with a logger. If the mice are tagged (via RFID or video tracking), it would be possible to determine which individual uses which wheel and when. The wireless aspect means minimal wiring in the enclosure, preserving the social/physical environment. This could enable studies of how social dynamics influence exercise (do dominant mice monopolize wheels? Do mice run more when housed with peers vs. alone?). Similarly, environmental enrichment studies can benefit: a wheel logger can be one of several enrichment objects in a cage, measuring how much it’s used relative to others.
- High-Throughput Phenotyping: In genetics or drug discovery, it’s useful to screen many subjects for subtle phenotypes. A wireless wheel system allows scientists to perform high-throughput phenotyping of circadian and activity traits. For example, one could simultaneously monitor the circadian wheel-running of 100 different knockout mouse lines to identify which genes alter activity patterns. Because the data are collected automatically, the limiting factor becomes data analysis rather than physical data gathering. Advanced analytics (potentially AI or machine learning) could be applied to the rich datasets to discover patterns – for instance, identifying clusters of behavior (some genotypes might show ultradian bursts of running, others very stable daily patterns, etc.). Without a wireless, automated system, such a large-scale study would be prohibitively labor-intensive.
- Real-Time Intervention and Feedback: A unique possibility with wireless logging is to create experiments where the animal’s behavior triggers a real-time intervention. Since the logger timestamps each revolution and sends it to a hub, software can detect certain patterns and respond. For instance, in a pharmacological context, if a mouse runs significantly more at night after receiving a drug, the system might automatically flag this or adjust the next dose timing. Or in an operant conditioning paradigm, running on the wheel could be linked to a reward or stimulus delivery controlled remotely. While traditionally wheels are just passive measures, a smart logger could effectively turn it into part of an interactive setup (e.g. lock the wheel after a certain number of revolutions to study motivation or fatigue, then unlock later – all without entering the cage).
In summary, the wireless running wheel logger’s ability to collect high-resolution, continuous data in a low-intrusion manner greatly expands what researchers can do. From basic behavioral phenotyping to disease monitoring and drug testing, this technology provides a richer dataset and the flexibility to innovate experimental designs. The table below highlights some example applications across different neuroscience domains and the specific insights gained from wheel-running data in each case.
Example Applications of Wheel-Running Data in Neuroscience Research
| Domain | Example Study | Wheel-Running Data – Metrics & Insights |
|---|---|---|
| Behavioral/Exercise Neuroscience | Exercise & brain plasticity – Brené et al., 2007 (Wheel running as antidepressant) | Mice with wheel access showed increased hippocampal neurogenesis and reduced depression-like behavior. Demonstrates that voluntary running (measured by distance/time on wheel) can enhance brain plasticity and mood, supporting exercise as a therapeutic strategy. Time-stamped data ensured mice indeed ran regularly, correlating activity levels with neurobiological changes. |
| Neurodegenerative Disease | Parkinson’s disease model – Lim et al., 2015 (“6-OHDA” lesion study) | 6-OHDA-lesioned rats had fewer running bouts, shorter distances, lower peak speeds, and slower acceleration to peak vs. controls. Wheel logs quantified these motor deficits and their persistence over 6 weeks, validating wheel-running as a tool to measure disease severity. In another PD model, long-term wheel exercise upregulated neuroprotective factors and preserved motor function, highlighting exercise benefits recorded via wheel activity. |
| Circadian & Sleep Research | Aging and circadian rhythms – Valentinuzzi et al., 1997 (Circadian study in aged mice) | Continuous wheel data revealed age-related changes: older mice had delayed and more variable activity onsets at night, took longer to adjust to new light schedules, and showed fragmented running (shorter active bouts, longer rest periods). Free-running period in constant darkness was longer in old mice. These insights required precise timing of wheel revolutions to detect subtle shifts in rhythm and bout structure, serving as a non-invasive measure of clock function and sleep fragmentation. |
| Pharmacological Testing | Drug effects on activity – e.g. Weber 2009, Rhodes 2001 (cited in Garland 2011) (Antidepressant & stimulant effects) | Wheel-running is sensitive to drugs: SSRIs (antidepressants) caused mice to run less on the wheel (indicative of lethargy or reduced motivation), while dopamine stimulants like Ritalin and cocaine suppressed high-speed running bouts in genetically high-runner mice. Timestamped logs pinpointed when activity dropped or resumed relative to drug administration, and distinguished between changes in total distance vs. changes in running pattern (e.g. fewer intense bouts). This helps in assessing drug side effects (sedation, stimulation) and therapeutic impacts on locomotion or circadian timing. |
Conclusion
It is clear that a wireless running wheel data logger is a versatile tool in neuroscience. It plays a critical role in behavioral assays of voluntary exercise, provides phenotypic readouts in neurodegenerative models, serves as an indispensable method in circadian rhythm research, and offers a convenient measure for pharmacological studies. The addition of precise timestamps for each revolution enriches the dataset, enabling analysis of bout structure, timing of activity, circadian phase, and sleep-like patterns – metrics that deepen our understanding of rodent behavior and physiology. Moreover, by minimizing human intervention, such loggers yield more authentic behavioral data while improving experimental throughput and consistency. As technology advances, these devices are unlocking new experimental paradigms: from long-term monitoring of disease progression to real-time behavioral interventions, researchers can ask questions that were previously impractical.
In sum, wireless wheel data loggers exemplify how innovative instrumentation can propel multiple fields of neuroscience, offering a common platform to quantitatively link running behavior with brain function, health, and disease across a variety of contexts.