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
{"title":"用智能手机移动数据预测骨折后恢复:一项概念验证研究。","authors":"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","doi":"10.2106/jbjs.24.01305","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nAfter 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.\r\n\r\nMETHODS\r\nWe 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.\r\n\r\nRESULTS\r\nAll 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).\r\n\r\nCONCLUSIONS\r\nThese 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.\r\n\r\nLEVEL OF EVIDENCE\r\nPrognostic Level III. See Instructions for Authors for a complete description of levels of evidence.","PeriodicalId":22625,"journal":{"name":"The Journal of Bone & Joint Surgery","volume":"25 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Post-Fracture Recovery with Smartphone Mobility Data: A Proof-of-Concept Study.\",\"authors\":\"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\",\"doi\":\"10.2106/jbjs.24.01305\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND\\r\\nAfter 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.\\r\\n\\r\\nMETHODS\\r\\nWe 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.\\r\\n\\r\\nRESULTS\\r\\nAll 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).\\r\\n\\r\\nCONCLUSIONS\\r\\nThese 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.\\r\\n\\r\\nLEVEL OF EVIDENCE\\r\\nPrognostic Level III. See Instructions for Authors for a complete description of levels of evidence.\",\"PeriodicalId\":22625,\"journal\":{\"name\":\"The Journal of Bone & Joint Surgery\",\"volume\":\"25 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Journal of Bone & Joint Surgery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2106/jbjs.24.01305\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Bone & Joint Surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2106/jbjs.24.01305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting Post-Fracture Recovery with Smartphone Mobility Data: A Proof-of-Concept Study.
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.