MétaCan
Menu
Back to cohort
Record W2759985285 · doi:10.2196/iproc.8295

Continuous Remote Monitoring of Vital Signs in Pediatric Population

2017· article· en· W2759985285 on OpenAlexvenueno aff
Dhwanil Damania

Bibliographic record

VenueIproceedings · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsVital signsMedical emergencyWearable computerBluetoothHealth careClearanceWirelessRemote patient monitoringMedicineTelemedicineComputer scienceEmbedded systemTelecommunicationsNursingSurgery

Abstract

fetched live from OpenAlex

Background: Wearable sensor technologies coupled with secure patient portals allow for continuous real-time data acquisition from a patient in a hospital or home setting. This combination renders more effective health-management system with faster patient-recovery times and reduced healthcare costs. VitalConnect Inc. has developed a fully-disposable, FDA-cleared (for adults) wireless biosensor (VitalPatch®) that is worn on chest. VitalPatch captures multiple medical-grade biometrics and transmits the physiological data continuously (via Bluetooth) for up to 96 hours to a relay (tablet, wireless hub). Like adults, pediatric patients (5-17 years of age) could also benefit from such small and low-profile biosensor that can collect and stream vital signs uninterruptedly to a secure patient-portal. This data can then be monitored by a clinical triage center or healthcare-provider. Objective: Continuous remote monitoring of heart rate (HR) and breathing rate (BR) is critical during acute or chronic illnesses. The primary purpose of this study was to evaluate the performance of the VitalConnect platform in pediatric subjects during activities of daily living (ADL) to confirm accuracy of sensor measurements for HR and BR with respect to a clinical reference device, Capnostream20 (Oridion). Additionally, information on comfort and usability of VitalPatch at home was assessed for the total duration of wear. Methods: Thirty-five children were enrolled in the study (24 between 9-17 years, 11 between 5-8 years). All subjects participated in 96-hour wear-period of the VitalPatch biosensor and performed one hour of in-lab protocol on Day1 of the wear duration. Each participant underwent stationary breathing exercises (spontaneous, metronome), ADLs with postural maneuvers and treadmill walking. The study was conducted in presence of a nurse after approval from Institutional Review Board. The performance of heart rate, respiration rate, posture and number of steps were assessed for each subject separately using the mean absolute error (MAE) between the true and measured values. The reference for posture and step count was manual observation. Results: During the stationary condition, MAE (across 35 children) for HR and BR was 2.1±1.0 beats per minute (BPM) and 2.2±0.8 breaths per minute (BrPM) respectively compared to Capnostream20. During ADLs, MAE was 4.3±2.7 BPM for HR and 3.7±1.7 BrPM for BR. Accuracy of posture (standing, supine, walking) was >93% and step-count was >95% overall. Moreover, 80% participants rated wearing VitalPatch ‘comfortable’ without any itchiness during 96-hour wear. Conclusions: The VitalPatch biosensor demonstrated clinically-acceptable accuracy compared to a standardized reference device for both heart rate and breathing rate. It also provided sufficiently accurate measures for activity in terms of posture and number of steps. Furthermore, when used for up to 4 days at home, there were no adverse events (i.e. rashes, dermatitis, mechanical skin injury) and user experience was satisfactory. The accuracy of vitals and ease-of-use in the home environment in children aged 5-17 illustrate the potential of VitalPatch as a non-invasive and cost-effective vital sign monitoring alternative. VitalPatch biosensor can be integrated into state-of-art devices to monitor vital signs for pediatric patients with diseases like congenital heart abnormalities and adolescent fatigue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.437
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIproceedingsSame topicMobile Health and mHealth ApplicationsFrench-language works237,207