Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study.
Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study.
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接受胰腺手术的围手术期患者自我报告症状负担的数字生物标记物：前瞻性纵向研究。
作者：
Low CA;Li M;Vega J;Durica KC;Ferreira D;Tam V;Hogg M;Zeh Iii H;Doryab A;Dey AK
癌症治疗可能会导致各种症状，损害生活质量和功能，但临床医生经常错过。智能手机和可穿戴传感器可以捕获指示症状负担的行为和生理变化，从而实现对波动症状的被动和远程实时监测本研究的目的是检查智能手机和Fitbit数据是否可以用于估计胰腺手术前后的日常症状负担。共有44名计划接受胰腺手术的患者参与了这项前瞻性纵向研究，并提供了足够的探头和自我报告的症状数据用于分析。参与者收集智能手机传感器和Fitbit数据，并在手术前至少两周开始，在住院恢复期间以及术后出院后长达60天内完成每日症状评级。从原始智能手机和Fitbit数据中提取了反映移动性和活动模式、睡眠、屏幕时间、心率和通信的日间行为特征，并用于将第二天分类为高或低症状负担，并根据每个人报告的典型症状水平进行调整。除了总体症状负担外，我们还专门检查了疼痛、疲劳和腹泻。使用光梯度增强机（LightGBM）的模型能够正确预测第二天是否是高症状日，准确率为73.5%，超过基线模型。最重要的传感器特征，用于区分高症状天与体力活动发作，睡眠，心率和位置。LightGBM模型预测第二天腹泻（准确率为79.0%），疲劳（准确率为75.8%）和疼痛（准确率为79.6%）的表现相似。结果表明，数字生物标志物可能有助于预测癌症手术前后患者报告的症状负担。尽管在这个小样本中的模型性能可能不足以用于临床实施，研究结果支持从急性病的老年患者收集移动的传感器数据的可行性，以及移动的传感对癌症患者被动监测的潜在临床价值，并表明来自许多患者已经拥有和使用的设备的数据可能有助于检测恶化的围手术期症状和触发just-及时采取症状管理措施。
Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms The aim of this study was to examine whether smartphone and Fitbit data could be used to estimate daily symptom burden before and after pancreatic surgery. A total of 44 patients scheduled for pancreatic surgery participated in this prospective longitudinal study and provided sufficient sensor and self-reported symptom data for analyses. Participants collected smartphone sensor and Fitbit data and completed daily symptom ratings starting at least two weeks before surgery, throughout their inpatient recovery, and for up to 60 days after postoperative discharge. Day-level behavioral features reflecting mobility and activity patterns, sleep, screen time, heart rate, and communication were extracted from raw smartphone and Fitbit data and used to classify the next day as high or low symptom burden, adjusted for each individual’s typical level of reported symptoms. In addition to the overall symptom burden, we examined pain, fatigue, and diarrhea specifically. Models using light gradient boosting machine (LightGBM) were able to correctly predict whether the next day would be a high symptom day with 73.5% accuracy, surpassing baseline models. The most important sensor features for discriminating high symptom days were related to physical activity bouts, sleep, heart rate, and location. LightGBM models predicting next-day diarrhea (79.0% accuracy), fatigue (75.8% accuracy), and pain (79.6% accuracy) performed similarly. Results suggest that digital biomarkers may be useful in predicting patient-reported symptom burden before and after cancer surgery. Although model performance in this small sample may not be adequate for clinical implementation, findings support the feasibility of collecting mobile sensor data from older patients who are acutely ill as well as the potential clinical value of mobile sensing for passive monitoring of patients with cancer and suggest that data from devices that many patients already own and use may be useful in detecting worsening perioperative symptoms and triggering just-in-time symptom management interventions.
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影响因子：
3.7
作者：
Andrews S;Ellis DA;Shaw H;Piwek L
通讯作者：
Piwek L
DOI：
10.1146/annurev-clinpsy-032816-044949
发表时间：
2017-05-08
影响因子：
18.4
作者：
Mohr DC;Zhang M;Schueller SM
通讯作者：
Schueller SM
影响因子：
2.3
作者：
Falchook, Aaron D;Tracton, Gregg;Chera, Bhishamjit S
通讯作者：
Chera, Bhishamjit S
使用智能手机收集心理科学中的行为数据：机遇，实际考虑和挑战。
DOI：
10.1177/1745691616650285
发表时间：
2016-11
期刊：
Perspectives on psychological science : a journal of the Association for Psychological Science
影响因子：
--
作者：
Harari GM;Lane ND;Wang R;Crosier BS;Campbell AT;Gosling SD
通讯作者：
Gosling SD
影响因子：
7.4
作者：
Low CA;Dey AK;Ferreira D;Kamarck T;Sun W;Bae S;Doryab A
通讯作者：
Doryab A