Turlip Ryan W, Chauhan Daksh, Ahmad Hasan S, Dagli Mert Marcel, Hu Bonnie Y, Chung Richard J, Ghenbot Yohannes, Gu Ben J, Patel Nisarg, Kim Richelle J, Kincaid Julia, Verma Akash, Yoon Jang W
Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Department of Neurosurgery, School of Medicine, University of Virginia, Charlottesville, VA, United States.
Front Surg. 2025 Aug 12;12:1613915. doi: 10.3389/fsurg.2025.1613915. eCollection 2025.
Objectively studying patient outcomes following surgery has been an important aspect of evidence-based medicine. The current gold-standard-patient reported outcomes measures-provides valuable information but have subjective biases. Smartphones, which passively collect data on physical activity such as daily steps, may provide objective and valuable insight into patient recovery and functional status. This study aims to provide a methodological guide for data collection and analysis of smartphone accelerometer data to assess clinical outcomes following surgery.
Patient health metrics-namely daily steps, distance travelled, and flights climbed-were extracted from patient smartphones using easy-to-download applications. These applications upload the data that smartphone accelerometers passively collect daily to a HIPAA compliant encrypted server while de-identifying the patient's personal health information. Patients were consented in multiple settings-synchronously during clinical visits or asynchronously over the phone-and could be enrolled during the initial pre-operative visit or well after the surgery. With the patient data acquired, the peri-operative window of selection is determined based on the needs to the study. The timeseries data is then statistically normalized to account for individual baselines and smoothened over a 14-day moving average to minimize noise. Mathematical analysis can be harnessed to study quantifiable recovery and decline periods, which provide continuous and nuanced insight into patient's health throughout their spine disease and treatment course. Additionally, integrating clinical variables permits computational machine models capable of predicting patient trajectories and guiding clinical decisioning.
Smartphones offer a new metric for studying patient well-being and outcomes after surgery. The research with them is in its nascent stages but further studies can potentially revolutionize our understanding of spinal disease.
客观研究手术后的患者预后一直是循证医学的一个重要方面。当前的金标准——患者报告结局测量——提供了有价值的信息，但存在主观偏差。智能手机可以被动收集诸如每日步数等身体活动数据，可能为患者恢复情况和功能状态提供客观且有价值的见解。本研究旨在为收集和分析智能手机加速度计数据以评估手术后的临床结局提供方法指南。
使用易于下载的应用程序从患者智能手机中提取患者健康指标，即每日步数、行进距离和爬楼层数。这些应用程序将智能手机加速度计每日被动收集的数据上传到符合健康保险流通与责任法案（HIPAA）的加密服务器，同时对患者的个人健康信息进行去识别处理。患者在多种场景下获得同意——在临床就诊时同步同意或通过电话异步同意——并且可以在术前首次就诊时或手术后很久才入组。获取患者数据后，根据研究需求确定围手术期的选择窗口。然后对时间序列数据进行统计归一化以考虑个体基线，并通过14天移动平均值进行平滑处理以最小化噪声。可以利用数学分析来研究可量化的恢复和衰退期，这能在患者整个脊柱疾病及治疗过程中提供对其健康状况的持续且细致入微的见解。此外，整合临床变量可使计算机模型能够预测患者轨迹并指导临床决策。
智能手机为研究手术后患者的健康状况和预后提供了一种新的指标。对其进行的研究尚处于起步阶段，但进一步的研究可能会彻底改变我们对脊柱疾病的理解。
