Bone fractures are among the most common orthopedic injuries, with lower limb fractures accounting for 47.3 million fractures globally in 2019 (Collaborators, 2021). Delayed healing, or failure to heal, is especially common in lower limb fractures, with complications reported to occur in 13.6% of femur and 11.7% of tibia fractures. Ekegren et al. (2018) Many non-modifiable factors affect the rate of bone union, such as fracture pattern, degree of soft tissue damage, age, sex, smoking status, and medical comorbidities (Hellwinkel et al., 2020). The orthopaedic surgeon has the greatest control over the mechanical environment of the fracture site, through implant choice, insertion method, and timing of post-operative weightbearing. Mechanical loading is essential for effective bone healing and reduces the risk of delayed union in lower-limb fractures. For this reason, partial and full weightbearing is encouraged as tolerated (Ma et al., 2023). However, increased pain levels can lead to decreased weightbearing and less effective participation in physical therapy, which can negatively impact recovery (Eickhoff et al., 2022; Majuta et al., 2015).

Long bone fractures heal through four distinct but overlapping biological phases (Marcucio et al., 2023; Molitoris et al., 2024a). Briefly, following fracture, a hematoma forms, which stops bleeding, contains bone fragments, and triggers a pro-inflammatory cascade critical to the repair response (Kolar et al., 2010; Xing et al., 2010). In mice, this pro-inflammatory phase typically spans the first 5 days post fracture. Bone healing then proceeds through two distinct processes: (1) along the cortical surfaces of the bone, skeletal progenitor cells differentiate to form bone directly, whereas (2) within the fracture gap, progenitors differentiate to chondrocytes and form a provisional cartilage matrix that bridges the fracture (Colnot, 2009). Cartilage in the fracture gap then transforms to bone, through the process of endochondral ossification (Bahney et al., 2014; Haffner-Luntzer et al., 2019; Hu et al., 2017; Wong et al., 2020a; Wong et al., 2020b; Julien et al., 2020; Zhou et al., 2014). In the final phase of healing, osteoclasts remodel the newly formed trabecular bone into cortical bone (Drissi and Sanjay, 2016).

Physical forces are significant contributors to healing progression (Morgan et al., 2008; Anani and Castillo, 2022; Augat et al., 2021). Early in fracture healing, local mechanical strain—shaped by the fracture pattern and fixation method—directs cellular proliferation and lineage differentiation (osteogenic, chondrogenic, or fibrotic). Later, gross loading of the fracture site, typically in the form of weightbearing, is critical to bone remodeling and consolidation. While moderate compressive forces are favorable for fracture healing, too little or too much compressive loading, torsional and sheer forces, can delay healing. Although the loads and geometry differ between mice and humans, the underlying strain principals are comparable, supporting the use of the mouse as a suitable preclinical model to study functional recovery.

What remains poorly understood is how the biological process of fracture healing correlates with functional behaviors in mice. And, importantly, there is a growing body of evidence suggesting that the paucity of preclinical functional outcome measures in fracture repair hinders translation of effective treatments from mice to humans (Barr and ett, 2015; Woolf, 2010; Clark, 2016; Shen et al., 2020; Gunderson et al., 2020). Standard quantifiable preclinical outcome measures of bone healing are rarely based on behavioral assessment, but rather are destructive, including, histological tissue evaluation, ex vivo microcomputed tomography (μCT), gene and protein expression analysis, and biomechanical bone quality testing. These analyses are time consuming, expensive, and typically require a large number of animals to obtain the different destructive data sets. Gait analysis (e.g., DigiGate or Catwalk), static and dynamic weight bearing assays, or scored locomotion scales (e.g., Basso-Beattie-Bresnahan) have been used infrequently in fracture healing studies, due to the specialized equipment needs, the challenge in applying these protocols to mice with fracture, and their time-consuming analysis (Haruki Nishimura et al., 2025; McVeigh et al., 2020). For this reason, there remains an important technology gap for rapid, unbiased, and non-destructive evaluation of clinically informed outcome measurements that can provide more quantitative assessments of fracture-induced pain and functional recovery.

Here, we present data from a longitudinal behavioral phenotyping study in mice in which we quantitatively track functional recovery (i.e., weightbearing and kinematic shifts) after long bone fractures of the lower limb. Our goal was to assess the impact of mechanical stability and sex on functional behavioral recovery after tibia fracture. To represent the clinically modifiable mechanical environment of the fracture site, we compared two different methods of fracture fixation, namely, intramedullary pin stabilized versus unstabilized fractures. Additionally, we assessed the contribution of the non-modifiable factor of sex, as presently the preclinical and clinical data are conflicting as to the influence of sex in fracture healing (Haruki Nishimura et al., 2025; Egol et al., 2009; Noori et al., 2020). First, using our behavioral phenotyping approach, we analyzed a large number of individual behavioral metrics. Next, we developed a novel graph theoretic approach that integrates many distinct outcomes into a single, comprehensive metric that quantifies the global behavioral state of each mouse during recovery from fracture. We hypothesize that a unified metric of post-fracture behavior will make it possible to establish the time window for functional recovery and that this metric will confirm sex-independent faster functional recovery in mice with modulated mechanical strain (i.e., stabilized fracture).

Although stabilized and unstabilized preclinical fracture models have been used frequently, no studies directly compared functional recovery between these models. The present study presents comprehensive behavioral phenotyping of the functional deficits and recovery time course of mice following fracture, when fracture fixation and sex are varied in a controlled manner. To accomplish this, we combined longitudinal behavioral imaging analysis, machine learning, and graph theory analysis to rapidly identify and quantify variables that could ultimately translate meaningfully to human changes in weightbearing and gait post-fracture. Consistent with clinical expectation, we found that tibia fractures with intramedullary stabilization present with less severe behavioral shifts compared to unstabilized fractures. On the other hand, we did not find that sex contributes to differences in functional healing when behavioral metrics were viewed together through our unified metric of recovery.

To date, there has been a critical gap in technology that can quickly and reliably longitudinally quantify functional recovery in rodent models following fracture. Existing tools for preclinical gait analysis include DigiGait and TreadScan, which use transparent treadmills to identify abnormalities in rodent walking patterns, or the CatWalk, which requires that animals are trained to walk along a narrow glass platform and then produces pressure maps of the mouse paws (Xu et al., 2019; Jacobs et al., 2014; Chen et al., 2017). The major limitation of these behavioral analyses is that the required training of each mouse is time consuming and also that the confined walking environment prevents animals from behaving naturally and ambulating freely. As our data clearly show, after a fracture, an animal’s balance and gait are compromised, limiting the effectiveness of forced gait tests which are highly dependent on stereotyped behaviors and parameters, including locomotor speed, consistent movement, and motivation (Pitzer et al., 2021). Data analysis using these methods is also laborious, making it challenging to perform high throughput screening. Here, we present the first use of the Blackbox (Zhang et al., 2022), a behavioral imaging system and illustrate how weightbearing and kinematic changes that occur during fracture injury and recovery can be readily monitored. This novel behavioral phenotyping approach rapidly identifies and quantifies fracture-related behavioral changes, overcoming many of the challenges associated with other systems.

To validate our behavioral phenotyping methodology, we chose to modulate clinically meaningful variables that, based on altered mechanobiology, are hypothesized to impact functional recovery following fracture. First, we changed the degree of fracture stabilization. There is substantial evidence that the interfragmentary motion at the fracture site is a major contributor to the extent of biological healing response. For long bone fractures, moderate motion and compressive forces promote the stem cell proliferation and differentiation required to support fracture healing. However, excessive motion (especially in sheer) or ridged fixation that eliminates mechanical loading can lead to non-union and chronic pain (Andrzejowski and Giannoudis, 2019; Hak et al., 2010; Tay et al., 2014; Brinker et al., 2013; Brinker et al., 2017). In clinical settings, this desired degree of mechanical loading for tibia fractures is most commonly achieved through surgical fixation with intramedullary nails. Intramedullary nails are believed to allow sufficient interfragmentary motion for optimal healing and enable patients to bear weight earlier (Gradl, 2014; Duan et al., 2012). We model this scenario in the mice with tibia fractures using intramedullary pin stabilization, which is well established in the field as a clinically relevant standard rodent model (Bonnarens and Einhorn, 1984). To model excessive motion we left the fractures unstabilized, which produces the same endochondral healing response, but with a high degree of interfragmentary motion (Le et al., 2001).

The post-fracture measurement time points were selected to ensure that behavior was recorded at each of the distinct phases of endochondral fracture healing (Marcucio et al., 2023; Bahney et al., 2019). For this reason, it was important to ensure that the post-operative analgesia provided by the sustained buprenorphine did not interfere with our analysis. Previous studies reported that sustained release buprenorphine can provide analgesia for up to 72 h in a fracture (Wolter et al., 2023) or hindpaw incision (Arthur et al., 2022) models, but less than 24 h in targeted inflammatory pain models (Larson et al., 2024). Thus, our first post-fracture recording at day 5, and by extension our extracted functional parameters, were not influenced by buprenorphine. Biologically, recordings at 5-DPF corresponds to the late hematoma phase, 7-DPF represents the soft/cartilage callus stage of healing, and 10-DPF captures a key timepoint during the conversion of cartilage to bone during endochondral ossification (Figures 1B,C). By 19-DPF, the callus has largely converted to trabeculated bone, with cortical bone remodeling occurring between 25- to 35-DPF.

We observed distinct functional recovery patterns in mice with pin-stabilized versus unstabilized fractures. The latter group demonstrated delayed weightbearing, take shorter steps, and their hind limb gait is more synchronous (normal gait is asynchronous). A limitation in the direct comparison of these two models is that our unstabilized fracture model also has more muscle damage adjacent to the fracture due to the three-point bending trauma. This trauma may have iatrogenic damage leading to fracture fragmentation when compared to the pin-stabilized model, which we make using a drill hole that permits better visualization of the pin insertion and ensures no fracture comminution. On the other hand, the pin-stabilized fracture can lead to some intraarticular injury during the pin insertion. Despite this limitation both models are common in the literature and correspond to clinically relevant fixation strategies (i.e., casting versus intramedullary nailing).

The clinical translatability of these findings is supported by human biomotion and loading studies that collectively demonstrate gait parameters and kinetic measures are reliable, objective indicators of functional recovery following lower extremity fractures (Agres et al., 2024; Alves et al., 2022a; Elsoe and Larsen, 2017; Larsen et al., 2017). Together these studies demonstrate weight bearing capacity, ground reaction forces, and gait symmetry directly reflect the restoration of underlying musculoskeletal function, with asymmetric patterns persisting up to 1 year, indicating incomplete recovery. Although weightbearing metrics do not directly translate to internal forces at the fracture site (Heyland et al., 2023), the predictable relationship between kinetic forces and internal joint loading, combined with progressive normalization of gait parameters over time, does establish gait analysis as both a diagnostic tool and quantitative outcome measure for assessing functional recovery. Our partial paw intensity metrics (i.e., percent weightbearing on toes, pad, heal), which considers the weight distribution across different parts of the hindpaw, also has complementary clinical support as proxy for functional fracture healing (Heyland et al., 2023; Falzarano et al., 2018).

Collectively, existing data on sex differences in fracture recovery are inconclusive as to whether there are clinically meaningful alterations in functional recovery or pain behavior (Ortona et al., 2023). Consistent with other studies (Pitzer et al., 2021; Broom et al., 2021), we first show that unfractured, age-matched female and male mice exhibit significantly different gait and kinematic parameters, likely due in part to the heavier gross weights of male mice relative to females. Consequently, all post-fracture behavioral data in our study were normalized to naïve mice of the same sex. After this adjustment, we did not detect major sex-specific differences in functional recovery patterns, supporting our earlier histomorophometric data showing equivalent bone healing between male and female mice response after the difference in animal weight was taken into account (Wong et al., 2020a). Consistent with our finding, Tawfik et al. (2020) did not find sex-specific differences in fracture responses using either von Frey fibers or gait analysis. On the other hand, as the Tawfik et al and other studies have shown sex-specific divergence in innate and adaptive immune cell response after fracture, with a stronger immune response to injury in females, the observed lack of behavioral differences between sexes was somewhat surprising a (Tawfik et al., 2020; Molitoris et al., 2024b) Clinical data have found sex differences in acute pain that are dependent upon the location of the fracture (Tighe et al., 2014) and in a non-fracture setting there is evidence that females have higher incidence of pain, are more sensitive to painful stimulation as assessed in the laboratory, and are more likely to develop chronic pain (45% incidence versus 31%) (Rollman and Lautenbacher, 2001; Fillingim et al., 2009; Mogil and Bailey, 2010; Tsang et al., 2008).

Interpreting high dimensional datasets is an ongoing challenge, particularly when analyzing complex behavioral changes in response to injury. To address this challenge, we introduced a novel graph theoretic framework to analyze our combined datasets. This approach enables a global representation of large, complex datasets into a single framework and facilitates estimation of simple and interpretable summary metrics. Our unified metric, the distance to the baseline node in graph theoretic space, made it possible to identify when fractured mice can reasonably be considered to have “recovered”. To the best of our knowledge, this is the first application of graph theory to analyze state changes in kinematic animal behaviors.

Despite differences in individual metrics at later fracture timepoints, our unified metric establishes that composite functional deficits only persist until 10 DPF, with both male and female mice largely recovered by 19 DPF. This functional timeline coincides with the shift from cartilage to bone in the fracture callus (Bahney et al., 2019). The biological timeline of healing is well established in the fracture literature and it is known that at 5 DPF there is a robust pro-inflammatory response and a large hematoma within the fracture gap; as expected all mice at 5 DPF presented with severe functional deficits. Functional recovery begins between 7 and 10 DPF, correlating with the formation and maturation of cartilage within the fracture callus. By 19 DPF there is substantial trabecular bone formation bridging the fracture and at this time point we found that there are no longer significant functional deficits in any of the mice. Other studies did report that gait-related changes persist (for 4–6 weeks) in mice with a femur fracture, however, these heal more slowly than tibia fractures, and these studies only reported the traditional single parameter view of functional recovery (Hofman et al., 2020; Magnusdottir et al., 2021).

This study is a critical first step to addressing a technology gap in quantifying functional recovery following fracture in a preclinical model system. With our behavioral phenotyping approach we can rapidly and sensitively capture, quantify, and interpret a broad array of weightbearing and kinematic parameters. Furthermore, our unified graph theoretic metric of fracture recovery is flexible and can easily be repurposed for other studies. Importantly, this metric was highly sensitive to system variations (surgical fixation and sex), even with only 5 animals per group, and enabled non-destructive longitudinal analysis. In future studies, we plan to integrate our behavioral phenotyping approach with biological and/or mechanical healing parameters of the fracture. We also aim to expand our behavioral phenotyping approach to improve screening of therapeutics that can accelerate functional bone healing and analgesics that treat fracture pain.

As to translational significance, our preclinical behavioral phenotyping approach correlates with biomotion and loading studies conducted in the clinic although functional recovery is likely dependent upon both fracture location and activity (Alves et al., 2022b). Behavioral phenotyping in the clinic could also benefit from including patient reported outcomes (PROs) related to pain or physical function scores, such as the lower extremity function score (LEFS, (Binkley et al., 1999)) in our case of tibia fracture. Importantly, we believe that the introduction of graph theory as a methodology can integrate multiple variables into a single outcome measure of functional fracture healing. Graph theory could immediately synthesize clinical data sets that include standard of care outcomes, such as radiographic scoring (mRUST), with PROs or with emerging techniques that to capture function (biomotion, loading, FIX-IT (Bhandari et al., 2013)) or biological status (blood based biomarkers to immune function or bone healing).