PDF(Pubmed)
Abstract：
BACKGROUND: The incentive spirometer is a basic and common medical device from which electronic health care data cannot be directly collected. As a result, despite numerous studies investigating clinical use, there remains little consensus on optimal device use and sparse evidence supporting its intended benefits such as prevention of postoperative respiratory complications.
OBJECTIVE: The aim of the study is to develop and test an add-on hardware device for data capture of the incentive spirometer.
METHODS: An add-on device was designed, built, and tested using reflective optical sensors to identify the real-time location of the volume piston and flow bobbin of a common incentive spirometer. Investigators manually tested sensor level accuracies and triggering range calibrations using a digital flowmeter. A valid breath classification algorithm was created and tested to determine valid from invalid breath attempts. To assess real-time use, a video game was developed using the incentive spirometer and add-on device as a controller using the Apple iPad.
RESULTS: In user testing, sensor locations were captured at an accuracy of 99% (SD 1.4%) for volume and 100% accuracy for flow. Median and average volumes were within 7.5% (SD 6%) of target volume sensor levels, and maximum sensor triggering values seldom exceeded intended sensor levels, showing a good correlation to placement on 2 similar but distinct incentive spirometer designs. The breath classification algorithm displayed a 100% sensitivity and a 99% specificity on user testing, and the device operated as a video game controller in real time without noticeable interference or delay.
CONCLUSIONS: An effective and reusable add-on device for the incentive spirometer was created to allow the collection of previously inaccessible incentive spirometer data and demonstrate Internet-of-Things use on a common hospital device. This design showed high sensor accuracies and the ability to use data in real-time applications, showing promise in the ability to capture currently inaccessible clinical data. Further use of this device could facilitate improved research into the incentive spirometer to improve adoption, incentivize adherence, and investigate the clinical effectiveness to help guide clinical care.
OBJECTIVE: The aim of the study is to develop and test an add-on hardware device for data capture of the incentive spirometer.
METHODS: An add-on device was designed, built, and tested using reflective optical sensors to identify the real-time location of the volume piston and flow bobbin of a common incentive spirometer. Investigators manually tested sensor level accuracies and triggering range calibrations using a digital flowmeter. A valid breath classification algorithm was created and tested to determine valid from invalid breath attempts. To assess real-time use, a video game was developed using the incentive spirometer and add-on device as a controller using the Apple iPad.
RESULTS: In user testing, sensor locations were captured at an accuracy of 99% (SD 1.4%) for volume and 100% accuracy for flow. Median and average volumes were within 7.5% (SD 6%) of target volume sensor levels, and maximum sensor triggering values seldom exceeded intended sensor levels, showing a good correlation to placement on 2 similar but distinct incentive spirometer designs. The breath classification algorithm displayed a 100% sensitivity and a 99% specificity on user testing, and the device operated as a video game controller in real time without noticeable interference or delay.
CONCLUSIONS: An effective and reusable add-on device for the incentive spirometer was created to allow the collection of previously inaccessible incentive spirometer data and demonstrate Internet-of-Things use on a common hospital device. This design showed high sensor accuracies and the ability to use data in real-time applications, showing promise in the ability to capture currently inaccessible clinical data. Further use of this device could facilitate improved research into the incentive spirometer to improve adoption, incentivize adherence, and investigate the clinical effectiveness to help guide clinical care.
摘要：
背景：鼓励性肺活量计是一种基本且通用的医疗设备，无法从中直接收集电子医疗保健数据。因此,尽管有大量研究调查临床应用，对于最佳器械使用,目前尚无共识,且支持其预期益处的证据很少,如预防术后呼吸系统并发症.
目的：该研究的目的是开发和测试用于激励肺活量计数据捕获的附加硬件设备。
方法：设计了一种附加设备，已建成,并使用反射式光学传感器进行测试，以识别普通激励肺活量计的容积活塞和流量线轴的实时位置。调查人员使用数字流量计手动测试了传感器液位精度和触发范围校准。创建并测试了有效的呼吸分类算法，以从无效的呼吸尝试中确定有效。为了评估实时使用情况，使用激励肺活量计和附加设备作为使用AppleiPad的控制器开发了一个视频游戏。
结果：在用户测试中，以99%(SD1.4%)的体积准确度和100%的流量准确度捕获传感器位置.中值和平均体积在目标体积传感器水平的7.5％（SD6％）内，和最大传感器触发值很少超过预期的传感器水平，在2种相似但不同的激励肺活量计设计上显示出与放置良好的相关性。呼吸分类算法在用户测试中显示出100％的灵敏度和99％的特异性，并且该设备作为视频游戏控制器实时操作而没有明显的干扰或延迟。
结论：创建了一种用于激励肺活量计的有效且可重复使用的附加设备，以允许收集以前无法访问的激励肺活量计数据，并演示物联网在普通医院设备上的使用。该设计显示出高传感器精度和在实时应用中使用数据的能力，显示出捕获当前无法访问的临床数据的能力的希望。进一步使用该设备可以促进对激励肺活量计的改进研究，以提高采用率，激励坚持，并探讨其临床效果,有助于指导临床护理。
目的：该研究的目的是开发和测试用于激励肺活量计数据捕获的附加硬件设备。
方法：设计了一种附加设备，已建成,并使用反射式光学传感器进行测试，以识别普通激励肺活量计的容积活塞和流量线轴的实时位置。调查人员使用数字流量计手动测试了传感器液位精度和触发范围校准。创建并测试了有效的呼吸分类算法，以从无效的呼吸尝试中确定有效。为了评估实时使用情况，使用激励肺活量计和附加设备作为使用AppleiPad的控制器开发了一个视频游戏。
结果：在用户测试中，以99%(SD1.4%)的体积准确度和100%的流量准确度捕获传感器位置.中值和平均体积在目标体积传感器水平的7.5％（SD6％）内，和最大传感器触发值很少超过预期的传感器水平，在2种相似但不同的激励肺活量计设计上显示出与放置良好的相关性。呼吸分类算法在用户测试中显示出100％的灵敏度和99％的特异性，并且该设备作为视频游戏控制器实时操作而没有明显的干扰或延迟。
结论：创建了一种用于激励肺活量计的有效且可重复使用的附加设备，以允许收集以前无法访问的激励肺活量计数据，并演示物联网在普通医院设备上的使用。该设计显示出高传感器精度和在实时应用中使用数据的能力，显示出捕获当前无法访问的临床数据的能力的希望。进一步使用该设备可以促进对激励肺活量计的改进研究，以提高采用率，激励坚持，并探讨其临床效果,有助于指导临床护理。
