Jaimini Utkarshani, Thirunarayan Krishnaprasad, Kalra Maninder, Venkataraman Revathy, Kadariya Dipesh, Sheth Amit
Department of Computer Sciene, Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University, Dayton, OH, United States.
Dayton Children Hospital, Dayton, OH, United States.
JMIR Pediatr Parent. 2018 Jul-Dec;1(2):e11988. doi: 10.2196/11988. Epub 2018 Nov 30.
In the traditional asthma management protocol, a child meets with a clinician infrequently, once in 3 to 6 months, and is assessed using the Asthma Control Test questionnaire. This information is inadequate for timely determination of asthma control, compliance, precise diagnosis of the cause, and assessing the effectiveness of the treatment plan. The continuous monitoring and improved tracking of the child's symptoms, activities, sleep, and treatment adherence can allow precise determination of asthma triggers and a reliable assessment of medication compliance and effectiveness. Digital phenotyping refers to moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices, in particular, mobile phones. The kHealth kit consists of a mobile app, provided on an Android tablet, that asks timely and contextually relevant questions related to asthma symptoms, medication intake, reduced activity because of symptoms, and nighttime awakenings; a Fitbit to monitor activity and sleep; a Microlife Peak Flow Meter to monitor the peak expiratory flow and forced exhaled volume in 1 second; and a Foobot to monitor indoor air quality. The kHealth cloud stores personal health data and environmental data collected using Web services. The kHealth Dashboard interactively visualizes the collected data.
The objective of this study was to discuss the usability and feasibility of collecting clinically relevant data to help clinicians diagnose or intervene in a child's care plan by using the kHealth system for continuous and comprehensive monitoring of child's symptoms, activity, sleep pattern, environmental triggers, and compliance. The kHealth system helps in deriving actionable insights to help manage asthma at both the personal and cohort levels. The Digital Phenotype Score and Controller Compliance Score introduced in the study are the basis of ongoing work on addressing personalized asthma care and answer questions such as, "How can I help my child better adhere to care instructions and reduce future exacerbation?"
The Digital Phenotype Score and Controller Compliance Score summarize the child's condition from the data collected using the kHealth kit to provide actionable insights. The Digital Phenotype Score formalizes the asthma control level using data about symptoms, rescue medication usage, activity level, and sleep pattern. The Compliance Score captures how well the child is complying with the treatment protocol. We monitored and analyzed data for 95 children, each recruited for a 1- or 3-month-long study. The Asthma Control Test scores obtained from the medical records of 57 children were used to validate the asthma control levels calculated using the Digital Phenotype Scores.
At the cohort level, we found asthma was very poorly controlled in 37% (30/82) of the children, not well controlled in 26% (21/82), and well controlled in 38% (31/82). Among the very poorly controlled children (n=30), we found 30% (9/30) were highly compliant toward their controller medication intake-suggesting a re-evaluation for change in medication or dosage-whereas 50% (15/30) were poorly compliant and candidates for a more timely intervention to improve compliance to mitigate their situation. We observed a negative Kendall Tau correlation between Asthma Control Test scores and Digital Phenotype Score as -0.509 (<.01).
kHealth kit is suitable for the collection of clinically relevant information from pediatric patients. Furthermore, Digital Phenotype Score and Controller Compliance Score, computed based on the continuous digital monitoring, provide the clinician with timely and detailed evidence of a child's asthma-related condition when compared with the Asthma Control Test scores taken infrequently during clinic visits.
在传统的哮喘管理方案中,儿童与临床医生见面的频率较低,每3至6个月一次,并且使用哮喘控制测试问卷进行评估。这些信息不足以及时确定哮喘控制情况、依从性、病因的精确诊断以及评估治疗方案的有效性。对儿童的症状、活动、睡眠和治疗依从性进行持续监测并改进跟踪,能够精确确定哮喘触发因素,并可靠地评估药物依从性和有效性。数字表型分析是指使用来自个人数字设备(特别是手机)的数据,对个体水平的人类表型进行实时量化。kHealth套件包括一个安装在安卓平板电脑上的移动应用程序,该应用程序会询问与哮喘症状、药物摄入、因症状导致的活动减少以及夜间觉醒相关的及时且与情境相关的问题;一个用于监测活动和睡眠的Fitbit;一个用于监测呼气峰值流量和1秒用力呼气量的Microlife峰值流量计;以及一个用于监测室内空气质量的Foobot。kHealth云存储使用网络服务收集的个人健康数据和环境数据。kHealth仪表盘以交互方式可视化所收集的数据。
本研究的目的是探讨使用kHealth系统对儿童的症状、活动、睡眠模式、环境触发因素和依从性进行持续全面监测,以收集临床相关数据,帮助临床医生诊断或干预儿童护理计划的可用性和可行性。kHealth系统有助于得出可采取行动的见解,以在个人和队列层面帮助管理哮喘。本研究中引入的数字表型评分和控制器依从性评分是正在进行的解决个性化哮喘护理工作的基础,并回答诸如“我如何帮助我的孩子更好地遵守护理指导并减少未来的病情加重?”等问题。
数字表型评分和控制器依从性评分通过使用kHealth套件收集的数据总结儿童的状况,以提供可采取行动的见解。数字表型评分使用关于症状、急救药物使用、活动水平和睡眠模式的数据来规范哮喘控制水平。依从性评分反映儿童对治疗方案的遵守程度。我们对95名儿童的数据进行了监测和分析,每名儿童参与为期1个月或3个月的研究。从57名儿童的病历中获取的哮喘控制测试分数用于验证使用数字表型评分计算出的哮喘控制水平。
在队列层面,我们发现37%(30/82)的儿童哮喘控制非常差,26%(21/82)控制不佳,38%(31/82)控制良好。在哮喘控制非常差的儿童(n = 30)中,我们发现30%(9/30)对控制器药物摄入的依从性很高,这表明需要重新评估药物或剂量的变化;而50%(15/30)依从性差,需要更及时的干预以提高依从性来缓解他们的状况。我们观察到哮喘控制测试分数与数字表型评分之间的肯德尔tau相关性为 -0.509(<.01)。
kHealth套件适用于从儿科患者收集临床相关信息。此外,与在门诊就诊时不频繁进行的哮喘控制测试分数相比,基于持续数字监测计算得出的数字表型评分和控制器依从性评分为临床医生提供了有关儿童哮喘相关状况的及时且详细的证据。