A Digital Health Solution for Child Growth Monitoring at Home: Testing the Accuracy of a Novel “GrowthMonitor” Smartphone Application to Detect Abnormal Height and Body Mass Indices
dc.contributor.author | Thaventhiran, T | |
dc.contributor.author | Orr, J | |
dc.contributor.author | Morris, JK | |
dc.contributor.author | Hsu, A | |
dc.contributor.author | Martin, L | |
dc.contributor.author | Davies, KM | |
dc.contributor.author | Harding, V | |
dc.contributor.author | Chapple, P | |
dc.contributor.author | Dunkel, L | |
dc.contributor.author | Storr, HL | |
dc.date.accessioned | 2023-12-19T12:16:34Z | |
dc.date.available | 2023-08-15 | |
dc.date.available | 2023-12-19T12:16:34Z | |
dc.date.issued | 2023-12 | |
dc.identifier.issn | 2949-7612 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/93058 | |
dc.description.abstract | Objective: To develop and evaluate a smartphone application that accurately measures height and provides notifications when abnormalities are detected. Patients and Methods: A total of 145 (75 boys) participants with a mean age ± SD of 8.7±4.5 years (range, 1.0-17.0 years), from the Children’s Hospital at Barts Health Trust, London, United Kingdom, were enrolled in the study. “GrowthMonitor” (UCL Creatives) iPhone application (GMA) measures height using augmented reality. Using population-based (UK-WHO) references, algorithms calculated height SD score (HSDS), distance from target height (THSDSDEV), and HSDS change over time (ΔHSDS). Pre-established thresholds discriminated normal/abnormal growth. The GMA and a stadiometer (Harpenden; gold standard) measured standing heights of children at routine clinic visits. A subset of parents used GMA to measure their child’s height at home. Outcome targets were 95% of GMA measurements within ±0.5 SDS of the stadiometer and the correct identification of abnormal HSDS, THSDSDEV, and ΔHSDS. Results: Bland-Altman plots revealed no appreciable bias in differences between paired study team GMA and stadiometer height measurements, with a mean of the differences of 0.11 cm with 95% limits of agreement of −2.21 to 2.42 cm. There was no evidence of greater bias occurring for either shorter/younger children or taller/older children. The 2 methods of measurements were highly correlated (R=0.999). GrowthMonitor iPhone application measurements performed by parents in clinic and at home were slightly less accurate. The κ coefficient indicated reliable and consistent agreement of flag alerts for HSDS (κ=0.74) and THSDSDEV (κ=0.88) between 83 paired GMA and stadiometer measurements. GrowthMonitor iPhone application yielded a detection rate of 96% and 97% for HSDS-based and THSDSDEV-based red flags, respectively. Forty-two (18 boys) participants had GMA calculated ΔHSDS using an additional height measurement 6-16 months later, and no abnormal flag alerts were triggered for ΔHSDS values. Conclusion: GrowthMonitor iPhone application provides the potential for parents/carers and health care professionals to capture serial height measurements at home and without specialized equipment. Reliable interpretation and flagging of abnormal measurements indicate the potential of this technology to transform childhood growth monitoring. | en_US |
dc.format.extent | 498 - 509 | |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Mayo Clinic Proceedings Digital Health | |
dc.rights | This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.title | A Digital Health Solution for Child Growth Monitoring at Home: Testing the Accuracy of a Novel “GrowthMonitor” Smartphone Application to Detect Abnormal Height and Body Mass Indices | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2023 The Author(s), published Elsevier Ltd. | |
dc.identifier.doi | 10.1016/j.mcpdig.2023.08.001 | |
pubs.issue | 4 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
pubs.volume | 1 | en_US |
dcterms.dateAccepted | 2023-08-15 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
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Except where otherwise noted, this item's license is described as This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.