Shear Brian M, Brodke Dane J, Hancock Gregory R, McGlone Patrick, Demyanovich Haley, Li Vivian, Bell Alice, Okhuereigbe David, Slobogean Gerard P, O'Toole Robert V, O'Hara Nathan N
Department of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland.
Department of Human Development and Quantitative Methodology, University of Maryland College of Education, College Park, Maryland.
J Bone Joint Surg Am. 2025 Jun 4;107(11):e57. doi: 10.2106/JBJS.24.01305. Epub 2025 Apr 28.
After a lower-extremity fracture, the patient's priority is to regain function. To date, our ability to measure function has been limited. However, high-fidelity sensors in smartphones continuously measure mobility, providing an expansive pre- and post-injury gait history. We assessed whether pre-injury mobility data, combined with demographic and injury data, reliably predicted post-fracture mobility.
We enrolled 107 adult patients (mean age, 45 years; 43% female, 62% White, 36% Black, 1% Asian, 1% more than one race) ≥6 months after the surgical treatment of a lower-extremity fracture. Consenting patients exported their Apple iPhone mobility metrics, including step count, walking speed, step length, walking asymmetry, and double-support time. We integrated these mobility measures with demographic and injury data. Using nonlinear modeling, we assessed whether pre-injury mobility metrics combined with baseline data predicted post-fracture mobility.
All models were well calibrated and had model fits ranging from an adjusted R 2 of 0.18 (walking asymmetry) to 0.61 (double-support time). Pre-injury function strongly predicted post-injury mobility in all models. After the injury, the average daily step count increased by 65 steps each week (95% confidence interval [CI], 56 to 75). Weekly gains were significantly greater within 6 weeks after the injury (92 daily steps per week; 95% CI, 58 to 127) than 20 to 26 weeks post-injury (19 daily steps per week; 95% CI, 11 to 27; p < 0.001). Greater pre-injury steps were associated with increased post-injury mobility (301 daily steps post-injury per 1,000 steps pre-injury; 95% CI, 235 to 367). Mean walking speed declined by 0.200 m/s (95% CI, -0.257 to -0.143) from injury to 8 weeks post-injury. From 12 to 26 weeks post-injury, the average walking speed increased by 0.071 m/s (95% CI, 0.044 to 0.097).
These proof-of-concept findings highlight the value of high-fidelity pre-injury mobility data in predicting recovery. Individualized recovery projections can provide patient-friendly counseling tools and useful clinical insight for surgeons.
Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.
下肢骨折后，患者的首要任务是恢复功能。迄今为止，我们测量功能的能力有限。然而，智能手机中的高保真传感器可持续测量活动能力，提供详尽的伤前和伤后步态记录。我们评估了伤前活动数据与人口统计学及损伤数据相结合能否可靠地预测骨折后的活动能力。
我们纳入了107例成年患者（平均年龄45岁；43%为女性，62%为白人，36%为黑人，1%为亚洲人，1%为多种族），这些患者在接受下肢骨折手术治疗6个月后。同意参与的患者导出了他们苹果iPhone的活动指标，包括步数、步行速度、步长、步行不对称性和双支撑时间。我们将这些活动指标与人口统计学及损伤数据进行了整合。使用非线性建模，我们评估了伤前活动指标与基线数据相结合能否预测骨折后的活动能力。
所有模型校准良好，模型拟合度范围从调整后R²为0.18（步行不对称性）到0.61（双支撑时间）。伤前功能在所有模型中都能强烈预测伤后的活动能力。受伤后，平均每日步数每周增加65步（95%置信区间[CI]，56至75）。受伤后6周内每周的增加量（每周92步；95%CI，58至127）显著大于受伤后20至26周（每周19步；95%CI，11至27；p<0.001）。伤前步数越多与伤后活动能力增加相关（伤前每1000步对应的伤后每日步数为301步；95%CI，235至367）。从受伤到受伤后8周，平均步行速度下降了0.200m/s（95%CI，-0.257至-0.143）。从受伤后12至26周，平均步行速度增加了0.071m/s（95%CI，0.044至0.097）。
这些概念验证性研究结果凸显了高保真伤前活动数据在预测恢复情况方面的价值。个性化的恢复预测可为患者提供友好的咨询工具，并为外科医生提供有用的临床见解。
预后性III级。有关证据水平的完整描述，请参阅《作者须知》。
