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      Analysis of real-world data on growth hormone therapy adherence using a connected injection device

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          Abstract

          Background

          Poor adherence to long-term recombinant human growth hormone (r-hGH) treatment can lead to suboptimal clinical outcomes; consequently, supporting and monitoring adherence is a crucial part of patient management. We assessed adherence to r-hGH treatment in children with growth disorders over 48 months using a connected monitoring device (easypod™), which automatically transmits adherence data via an online portal (easypod™ connect); both sit within an adherence decision support system (ADSS). We also investigated the effect of age and sex on adherence.

          Methods

          Data from children transmitting over 10 injections between January 2007 and February 2019 were analyzed. Adherence (mg injected/mg prescribed) was categorized as high (≥85%), intermediate (> 56–84%) or low (≤56%) and assessed at seven time points from the start of treatment up to 48 months. Adherence was investigated over time and stratified by puberty status and sex. Mean transmission rate in each adherence category (total number of transmissions/total number of children) at each time point was calculated as a proxy measure of engagement in disease and treatment management. Descriptive analyses were performed.

          Results

          Longitudinal records were available for 13,553 children. Overall, 71% ( n = 9578) had high adherence, 22% ( n = 2989) intermediate and 7% ( n = 986) low. The proportion of children with high adherence decreased over time from 87% ( n = 12,964) to 65% ( n = 957) and was higher in pre-pubertal than pubertal children (girls: 80% [ n = 1270] vs 70% [ n = 4496]; boys 79% [ n = 2573] vs 65% [ n = 5214]). Children with high adherence had a higher mean number of transmissions (12.5 [SD 24.9]) than children with intermediate (7.2 [SD 15.3]) or low (3.5 [SD 5.7]) adherence.

          Conclusions

          High adherence was seen in patients administering r-hGH using the connected device. Children with high adherence were most likely to regularly transmit data. Pubertal children showed lower adherence. We show the potential to develop an ADSS to analyze trends in real-world adherence data. This may prove useful to direct interventions to improve adherence while the ability to readily share data with healthcare professionals may itself improve adherence.

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          Internet of Things for Smart Cities

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            Mobile devices and apps for health care professionals: uses and benefits.

            Health care professionals' use of mobile devices is transforming clinical practice. Numerous medical software applications can now help with tasks ranging from information and time management to clinical decision-making at the point of care.
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                Author and article information

                Contributors
                ekaterina.koledova@merckgroup.com
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                29 July 2020
                29 July 2020
                2020
                : 20
                : 176
                Affiliations
                [1 ]GRID grid.39009.33, ISNI 0000 0001 0672 7022, Endocrinology Global Medical, Safety and CMO, Merck KGaA, ; 64293 Darmstadt, Germany
                [2 ]GRID grid.418389.f, ISNI 0000 0004 0403 4398, Merck Connected Health and Devices, Ares Trading S.A., an affiliate of Merck KGaA, ; 1262 Eysins, Switzerland
                [3 ]GRID grid.4858.1, ISNI 0000 0001 0208 7216, The Netherlands Organization for Applied Scientific Research TNO, ; Leiden, The Netherlands
                Author information
                http://orcid.org/0000-0003-2572-9052
                Article
                1183
                10.1186/s12911-020-01183-1
                7389874
                32727461
                64e93d5d-ff6e-4fdb-ba47-0750ee7cac7d
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 8 January 2020
                : 10 July 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100009945, Merck KGaA;
                Award ID: N/A
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Bioinformatics & Computational biology
                adherence,ehealth,electromechanical injection device,growth disorders,growth hormone,population health

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