Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study.
Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study.
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DOI：
10.2196/jmir.9046
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发表时间：
2017-12-19
影响因子：
7.4
通讯作者：
Doryab A
中科院分区：
文献类型：
--
作者：
Low CA;Dey AK;Ferreira D;Kamarck T;Sun W;Bae S;Doryab A
癌症患者化疗期间常见的生理和心理症状，实时监测这些症状可以改善患者的预后。嵌入在手机中的传感器和可穿戴的活动跟踪器可能会在被动监测症状方面发挥潜在作用，患者的负担最小。这项研究的目的是探索被动感知的手机和Fitbit数据是否可以用来估计化疗期间的日常症状负担。共有14名接受胃肠道癌症化疗的患者参与了这项为期4周的研究。参与者在研究期间携带Android手机，佩戴Fitbit设备，并完成了12种常见症状的日常严重程度评级。症状严重程度评分相加以创建每天的总症状负担评分，评分以单个患者的平均值为中心，并分为低、中、高症状负担天数。从原始移动电话传感器和Fitbit数据中提取日级特征，包括反映移动性和活动、睡眠、手机使用(例如，与手机和应用程序交互的持续时间)和通信(例如，呼入和呼出电话和消息的数量)的特征。我们使用交叉验证和重采样替换的旋转随机森林分类器来评估总体和个体模型的性能，并使用基于相关性的特征子集选择来选择预测能力最好的非冗余特征。在295天的症状和传感器数据数据中，许多手机和Fitbit功能与患者报告的症状负担得分相关。我们的人口模型的准确率达到了88.1%。准确度最高的功能包括久坐不动是最频繁的活动，轻微体力活动的时间较少，手机的变化性和平均加速较小，屏幕显示时间和与手机上的应用程序的互动时间更长。手机功能比Fitbit功能具有更好的预测能力。单个模型的准确率从78.1%到100%(平均为88.4%)，相关特征的子集因参与者而异。被动传感器数据，包括手机加速计和使用情况以及Fitbit评估的活动和睡眠，与化疗期间的日常症状负担有关。这些发现突出了在化疗期间以最小的患者负担对癌症患者进行长期监测的机会，以及旨在早期管理恶化或严重症状的实时适应性干预。
Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden. The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy. A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability. Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants. Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms.
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影响因子：
7.4
作者：
Saeb S;Zhang M;Karr CJ;Schueller SM;Corden ME;Kording KP;Mohr DC
通讯作者：
Mohr DC
DOI：
10.1038/nrclinonc.2012.88
发表时间：
2012-05-29
期刊：
Nature reviews. Clinical oncology
影响因子：
--
作者：
通讯作者：
--
影响因子：
50.5
作者：
Weaver, A.;Young, A. M.;Tarassenko, L.
通讯作者：
Tarassenko, L.
影响因子：
3.7
作者：
Christensen, Matthew A.;Bettencourt, Laura;Marcus, Gregory M.
通讯作者：
Marcus, Gregory M.
影响因子：
7.4
作者：
Min YH;Lee JW;Shin YW;Jo MW;Sohn G;Lee JH;Lee G;Jung KH;Sung J;Ko BS;Yu JH;Kim HJ;Son BH;Ahn SH
通讯作者：
Ahn SH