Predicting Post-Fracture Recovery with Smartphone Mobility Data: A Proof-of-Concept Study.

Brian M Shear,Dane J Brodke,Gregory R Hancock,Patrick McGlone,Haley Demyanovich,Vivian Li,Alice Bell,David Okhuereigbe,Gerard P Slobogean,Robert V O'Toole,Nathan N O'Hara
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Abstract

BACKGROUND 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. METHODS 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. RESULTS All models were well calibrated and had model fits ranging from an adjusted R2 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). CONCLUSIONS 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. LEVEL OF EVIDENCE Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
用智能手机移动数据预测骨折后恢复:一项概念验证研究。
背景: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以持续测量移动能力,提供广泛的损伤前后步态历史。我们评估了损伤前的活动能力数据,结合人口统计学和损伤数据,是否能可靠地预测骨折后的活动能力。方法纳入107例成人患者(平均年龄45岁;43%女性,62%白人,36%黑人,1%亚洲人,1%超过一个种族)下肢骨折手术治疗后≥6个月。同意的患者导出他们的苹果iPhone移动指标,包括步数、步行速度、步长、步行不对称和双支撑时间。我们将这些移动测量与人口统计和伤害数据相结合。通过非线性建模,我们评估了损伤前活动能力指标结合基线数据是否能预测骨折后的活动能力。结果所有模型均经过良好的校正,模型拟合的校正R2为0.18(行走不对称)至0.61(双支撑时间)。在所有模型中,损伤前功能都能预测损伤后的活动能力。损伤后,平均每日步数每周增加65步(95%置信区间[CI], 56至75)。在受伤后的6周内,每周的收益显著增加(每周92步;95% CI, 58 - 127)大于伤后20 - 26周(每周19步;95% CI, 11 ~ 27;P < 0.001)。更大的损伤前步数与损伤后活动能力增加相关(损伤后每1000步每天301步;95% CI, 235 ~ 367)。损伤后8周,平均步行速度下降了0.200 m/s (95% CI, -0.257 ~ -0.143)。损伤后12 ~ 26周,平均步行速度提高0.071 m/s (95% CI, 0.044 ~ 0.097)。结论这些概念验证的研究结果强调了高保真的损伤前运动数据在预测恢复方面的价值。个性化的康复预测可以为外科医生提供患者友好的咨询工具和有用的临床见解。证据水平:预后III级。有关证据水平的完整描述,请参见作者说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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