Evaluation of a new, wireless pulse oximetry monitoring system in infants: the BBA bootee

Rimet, Y.; Brusquet, Y; Ronayette, D; Dageville, C.; Lubrano, M.; Mallet, E.; Rambaud, Caroline; Terlaud, C.; Netchiporouk, Larissa I.; Blanc, David Le; Silve, Jérôme; Lerda, Olivier; Klefstad-Sillonville, Francis; Weber, Jean-Luc

doi:10.1007/978-3-540-70994-7_25

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…Regular information campaigns and audits should keep staff updated. Unobtrusive wireless heart rate monitors are a technology that has interesting potentials, but an evaluation of the potential benefits of electronic monitoring to help clinical staff during the first neonatal hours in thousands of healthy newborns is needed . S‐SSC integrates the benefits of early parental bonding with safe breastfeeding practice and gentle monitoring of the potentially vulnerable newborn.…”

Section: Discussionmentioning

confidence: 99%

Unexpected collapse of healthy newborn infants: risk factors, supervision and hypothermia treatment

Pejovic

Herlenius

2013

Acta Paediatrica

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AimTo determine the occurrence and risk factors of sudden unexpected postnatal collapse (SUPC) in presumably healthy newborn infants.MethodsAll live-born infants during a 30-month period, in five major delivery wards in Stockholm, were screened, and possible cases of SUPC thoroughly investigated. Infants were ≥35 weeks of gestation, had an Apgar score >8 at 10 min and collapsed within 24 h after birth. Maternal, infant, event characteristics and outcome data were collected.ResultsTwenty-six cases of SUPC were found among 68 364 live-born infants, an incidence of 38/100 000 live births. Sixteen of these cases of SUPC required resuscitation with ventilation >1 min, and 14 of these remained unexplained (21/100 000). Fifteen of the 26 children were found in a prone position, during skin-to-skin contact, 18 were primipara, and 13 occurred during unsupervised breastfeeding at <2 h of age. Three cases occurred during smart cellular phone use by the mother. Five developed hypoxic–ischaemic encephalopathy (HIE) grade 2, and 4 underwent hypothermia treatment. Twenty-five infants had a favourable neurological outcome.ConclusionSUPC in apparent healthy babies is associated with initial, unsupervised breastfeeding, prone position, primiparity and distractions. Guidelines outlining the appropriate monitoring of newborns and safe early skin-to-skin contact should be implemented.

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Section: Discussionmentioning

confidence: 99%

Unexpected collapse of healthy newborn infants: risk factors, supervision and hypothermia treatment

Pejovic

Herlenius

2013

Acta Paediatrica

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“…Many wireless pulse oximeter devices, like OSS3, are under investigation. 2,3,5,[10][11][12][13] Currently, there is no FDA-approved wireless pulse oximeter for NICU use. This was the first observational study to evaluate the accuracy of the OSS3 in preterm or <2.5 kg infants.…”

Section: Discussionmentioning

confidence: 99%

“…Different wireless devices had been compared with other standard wired devices. 2,3,5,[10][11][12][13] Most of these studies were compared against different wired devices than the one used in this study nor against SaO 2 , which prevented direct comparison between our study results with other wireless devices. Keerthy and Nagesh has shown that a wireless pulse oximeter only has 1.2% SpO 2 measurement bias against a wired device.…”

Section: Discussionmentioning

confidence: 99%

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Accuracy of Wireless Pulse Oximeter on Preterm or <2.5 kg Infants

et al. 2023

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Objective Monitoring heart rate (HR) and oxygen saturation (SpO2) in infants is essential in the neonatal intensive care unit. Wireless pulse oximeter technology has been advancing but with limited accuracy data on preterm infants. This observational study compared HR and SpO2 of the wireless Owlet Smart Sock 3 (OSS3) to the wired Masimo SET (Masimo) pulse oximeter in preterm or <2.5 kg infants. Study Design Twenty-eight eligible infants were enrolled. They weighed between 1.7 and 2.5 kg and were without anomalies or medical instability. OSS3 and Masimo simultaneously monitored HR and SpO2 for 60 minutes. The data were aligned by time epoch and filtered for poor tracings. The agreement was compared using the Pearson's correlation coefficient, the Bland–Altman method, average root mean square (ARMS), and prevalence and bias adjusted kappa (PABAK) analyses. Results Two infants' data were excluded due to motion artifacts or device failures. The corrected gestational age and current weights were 35 ± 3 weeks and 2.0 ± 0.2 kg (mean ± standard deviation), respectively. Over 21 hours of data showed that HR was strongly correlated between the two devices (r = 0.98, p < 0.001), with a difference of −1.3 beats per minute (bpm) and the limit of agreement (LOA) −6.3 to 3.4 bpm based on the Bland–Altman method. SpO2 was positively correlated between the two devices (r = 0.71, p < 0.001) with a SpO2 bias of 0.3% (LOA: −4.6 to 4.5%). The estimated ARMS of OSS3 compared with Masimo was 2.3% for SpO2 in the 70 to 100% range. The precision decreased with lower SpO2. A strong agreement (PABAK = 0.94) was between the two devices on whether SpO2 was above or below 90%. Conclusion OSS3 provided comparable HR and SpO2 accuracy to Masimo in preterm or <2.5 kg infants. Motion artifacts, lack of arterial blood gas comparisons, and lack of racial and ethnic diversity are the study limitations. More OSS3 data on the Lower HR and SpO2 ranges were needed before implementing inpatient use. Key Points

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“…The web site also generates SMS and e-mail alerts as required (Waugh, 2012). Other reported approaches include pulse oximetry monitoring (Rimet et al, 2007), wireless audio sensing (Al-Dasoqi et al, 2010), and UWB radar sensing (Ziganshin et al, 2010).…”

Section: Baby Sleep Monitoringmentioning

confidence: 99%

Wellness, Fitness, and Lifestyle Sensing Applications

McGrath¹,

Scanaill²

2013

Sensor Technologies

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Physical fitness is not only one of the most important keys to a healthy body; it is the basis of dynamicand creative intellectual activity. -John F. Kennedy, 35th President of the United StatesChapter 9 considered how sensors are playing an increasingly important role in health-related applications, such as chronic disease management. Medically, health is sometimes described as the absence of one or more of the "five Ds": death, disease, discomfort, disability, and dissatisfaction. Consequently, the focus is on determining whether disease is present and, when present, managing that condition (Edlin et al., 2000). Wellness takes a different perspective on health. It looks at the entire person, the manner in which they live their life, and lifestyle influences on their well-being. Wellness encompasses six distinct dimensions of well-being: emotional, intellectual, spiritual, occupational, social, and physical (Hettler, 1976). Collectively, these dimensions are often referred to as the holistic model of wellness. Sensors can be applied to quantify all dimensions of wellness to a certain extent. Of these dimensions, only physical wellness is monitored by individuals in the consumer domain. This chapter focuses on physical well-being and how sensing can be used to monitor and maintain physical wellness. Positively influencing physical well-being can also have significant benefits for other aspects of well-being, such as socializing with others during physical activities and helping to reduce emotional stress.A variety of factors can influence personal wellness, including diet, exercise, poor habits, proactive self-care, and seeking medical intervention when appropriate (Edlin et al., 2014). As such, wellness is a dynamic process that is constantly changing based on the daily decisions we make about what we eat, drink, how much exercise we do, and so on. It is easy to lose track of wellness with the demands of busy, modern lifestyles. Technology is now having a positive effect on individuals by helping them to manage their physical wellness. This trend will continue to grow in the future as sensing and supporting technologies are seamlessly integrated into our daily lives. Discrete sensors and sensors integrated into smartphones are already enabling us to monitor our activity levels, fitness, performance levels, and calorie burn/consumption through smartphone apps and web portals. In a broader context, pervasive sensing in our homes and leisure areas will provide passive monitoring on a long-term basis of our physical activities, interactions with our environment, and other physiological, cognitive, and biochemical parameters of interest without activity restriction and behavior modification. The collected data can be used to notify us of immediate risk or to identify trends in parameters that are outside of normative ranges. Sensing sleep quality, in babies and adults, is a now a common application of pervasive sensing. The consumer does not actively track their sleep in real time but they need to be alerted immedi...

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Evaluation of a new, wireless pulse oximetry monitoring system in infants: the BBA bootee

Cited by 3 publications

References 11 publications

Unexpected collapse of healthy newborn infants: risk factors, supervision and hypothermia treatment

Unexpected collapse of healthy newborn infants: risk factors, supervision and hypothermia treatment

Accuracy of Wireless Pulse Oximeter on Preterm or <2.5 kg Infants

Wellness, Fitness, and Lifestyle Sensing Applications

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