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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2020

Open Access 01-12-2020 | Research article

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

Authors: Ekaterina Koledova, Vincenzo Tornincasa, Paula van Dommelen

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

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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.
Literature
1.
Richmond E, Rogol AD. Current indications for growth hormone therapy for children and adolescents. Endocr Dev. 2010;18:92–108.PubMedCrossRef
2.
Cutfield WS, Derraik JG, Gunn AJ, Reid K, Delany T, Robinson E, Hofman PL. Non-compliance with growth hormone treatment in children is common and impairs linear growth. PLoS One. 2011;6(1):e16223.PubMedPubMedCentralCrossRef
3.
Hartmann K, Ittner J, Muller-Rossberg E, Schonau E, Stephan R, Ullrich KP, Hoppe B, Ramseger R, Bramswig J. Growth hormone treatment adherence in prepubertal and pubertal children with different growth disorders. Horm Res Paediatr. 2013;80(1):1–5.PubMedCrossRef
4.
Rosenfeld R, Bakker B. Compliance and persistence in pediatric and adult patients receiving growth hormone therapy. Endocr Pract. 2008;14(2):143–54.PubMedCrossRef
5.
Bozzola M, Colle M, Halldin-Stenlid M, Larroque S, Zignani M. Treatment adherence with the easypod growth hormone electronic auto-injector and patient acceptance: survey results from 824 children and their parents. BMC Endocr Disord. 2011;11:4.PubMedPubMedCentralCrossRef
6.
Stoyanov G KE, Vandermeulen J & the Canadian ECOS Group: Objectively measured treatment adherence in the easypod™ connect observational study (ECOS). Canadian interim analysis: population data and case reports. In: CPEG. Edmonton, Alberta, Canada: CPEG; 2016.
7.
8.
Lee MK, Lee KH, Yoo SH, Park CY. Impact of initial active engagement in self-monitoring with a telemonitoring device on glycemic control among patients with type 2 diabetes. Sci Rep. 2017;7(1):3866.PubMedPubMedCentralCrossRef
9.
10.
Ebner H, Hayn D, Kropf M, Modre-Osprian R, Schreier G. Internet of things based medication adherence assessment. Stud Health Technol Inform. 2016;221:118.PubMed
11.
Lakshmi Dhevi B, Vishvaksenan KS, Senthamil Selvan K, Rajalakshmi A. Patient monitoring system using cognitive internet of things. J Med Syst. 2018;42(11):229.PubMedCrossRef
12.
Schreier G. The internet of things for personalized health. Stud Health Technol Inform. 2014;200:22–31.PubMed
13.
Stegerman SBJ, Cerreta F, Chanie E, Leofgren A, Maio M, Schreier G, Thesing-Bleck E. Adherence measurement systems and technology for medications in older patient populations. Eur Geriatr Med. 2012;3:254–60.CrossRef
14.
Althoff T, White RW, Horvitz E. Influence of Pokemon Go on Physical Activity: Study and Implications. J Med Internet Res. 2016;18(12).
15.
16.
17.
Fernandez-Luque L, Labarta JI, Palmer E, Koledova E. Content analysis of apps for growth monitoring and growth hormone treatment: systematic search in the android app store. JMIR Mhealth Uhealth. 2020;8(2):e16208.PubMedPubMedCentralCrossRef
18.
Zanella ABN, Castellani A, Vangelista L, Zorzi M. Internet of things for Smart cities. IEEE Internet Things J. 2014;1:22–32.CrossRef
19.
20.
Ma LN, Nguyen, H, Mirza, F, Neuland O. Two way architecture netween IOT sensors and cloud computing for remote health care monitoring applications. In: 25th European Conference on Information Systems (ECIS): June 5-10, 2017. Guimarães: Research-in-Progress Papers; 2017. p. 2834–41.
21.
Misdrahi D, Tessier A, Husky M, Lange AC, Vrijens B, Llorca PM, Bayle FJ. Evaluation of adherence patterns in schizophrenia using electronic monitoring (MEMS(R)): a six-month post-discharge prospective study. Schizophr Res. 2018;193:114–8.PubMedCrossRef
22.
Forbes CA, Deshpande S, Sorio-Vilela F, Kutikova L, Duffy S, Gouni-Berthold I, Hagstrom E. A systematic literature review comparing methods for the measurement of patient persistence and adherence. Curr Med Res Opin. 2018;34(9):1613–25.PubMedCrossRef
23.
24.
Clifford S, Perez-Nieves M, Skalicky AM, Reaney M, Coyne KS. A systematic literature review of methodologies used to assess medication adherence in patients with diabetes. Curr Med Res Opin. 2014;30(6):1071–85.PubMedCrossRef
25.
Toussi M, Choleau C, Reach G, Cahane M, Bar-Hen A, Venot A. A novel method for measuring patients' adherence to insulin dosing guidelines: introducing indicators of adherence. BMC Med Inform Decis Mak. 2008;8:55.PubMedPubMedCentralCrossRef
26.
Diemert S, Weber J, Price M, Bannman J. Data for adherence decision support. Stud Health Technol Inform. 2019;257:75–9.PubMed
27.
Dahlgren J. Easypod: a new electronic injection device for growth hormone. Expert Rev Med Devices. 2008;5(3):297–304.PubMedCrossRef
28.
Koledova E, Stoyanov G, Ovbude L, Davies PSW. Adherence and long-term growth outcomes: results from the easypod connect observational study (ECOS) in paediatric patients with growth disorders. Endocr Connect. 2018;7(8):914–23.PubMedPubMedCentralCrossRef
29.
Koledova E, Stoyanov G, Ovbude L, Davies PSW. Adherence and long-term growth outcomes: results from the easypod() connect observational study (ECOS) in paediatric patients with growth disorders. Endocr Connect. 2018;7(8):914–23.PubMedPubMedCentralCrossRef
30.
van Dommelen P, Koledova E, Wit JM. Effect of adherence to growth hormone treatment on 0-2 year catch-up growth in children with growth hormone deficiency. PLoS One. 2018;13(10):e0206009.PubMedPubMedCentralCrossRef
31.
Dawson LA. What factors affect adherence to medicines? Arch Dis Child Educ Pract Ed. 2019;104(1):49–52.
32.
Dunn TC, Xu Y, Hayter G, Ajjan RA. Real-world flash glucose monitoring patterns and associations between self-monitoring frequency and glycaemic measures: a European analysis of over 60 million glucose tests. Diabetes Res Clin Pract. 2018;137:37–46.PubMedCrossRef
33.
Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK biobank participants. Sci Rep. 2018;8(1):7961.PubMedPubMedCentralCrossRef
34.
Forestier B, Anthoine E, Reguiai Z, Fohrer C, Blanchin M. A systematic review of dimensions evaluating patient experience in chronic illness. Health Qual Life Outcomes. 2019;17(1):19.PubMedPubMedCentralCrossRef
35.
Benson T. Measure what we want: a taxonomy of short generic person-reported outcome and experience measures (PROMs and PREMs). BMJ Open Qual. 2020;9(1).
36.
37.
Fedele DA, Cushing CC, Fritz A, Amaro CM, Ortega A. Mobile health interventions for improving health outcomes in youth: a meta-analysis. JAMA Pediatr. 2017;171(5):495.CrossRef
38.
Radovick SHE, Kalisvaart A, Koning M, Paridaens K, Boulos MNK. Gamification concepts to promote and maintain therapy adherence in children with growth hormone deficiency. J Multidiscip Sci J. 2018;1(1):71–80.
39.
Lu AS, Kharrazi H, Gharghabi F, Thompson D. A systematic review of health videogames on childhood obesity prevention and intervention. Games Health J. 2013;2(3):131–41.PubMedPubMedCentralCrossRef
40.
DeShazo J, Harris L, Pratt W. Effective intervention or Child’s play? A review of video games for diabetes education. Diabe Technol. 2010;12(10):815–22.CrossRef
41.
Bartholomew LK, Gold RS, Parcel GS, Czyzewski DI, Sockrider MM, Fernandez M, Shegog R, Swank P. Watch, discover, think, and act: evaluation of computer-assisted instruction to improve asthma self-management in inner-city children. Patient Educ Couns. 2000;39(2–3):269–80.PubMedCrossRef
42.
43.
44.
Gabarron E, Serrano JA, Wynn R, Armayones M. Avatars using computer/smartphone mediated communication and social networking in prevention of sexually transmitted diseases among north-Norwegian youngsters. BMC Med Inform Decis Mak. 2012;12:120.PubMedPubMedCentralCrossRef
45.
A-FC A-EA, Saavedra-Castillo E, Barrientos-Pérez M, Rivero-Escalante H, Flores-Caloca O, Calzada León R, Valdez-Morales F, Koledova E, Blanco-López A. The Easypod Connect Observational Study (ECOS): descriptive analysis of adherence to treatment of growth hormone deficient and small for gestational age naïve to easypod patients in Mexico 2012–2015. Horm Res Paediatr. 2016;86:103.
46.
CR NM, Tauber M, López Y. Easypod™ Connect Observational Study (ECOS) – French Case Histories And Growth Outcomes. Horm Res Paediatr. 2017;88(Supplement 1):1–830.
47.
Sayeed R, Gottlieb D, Mandl KD. SMART markers: collecting patient-generated health data as a standardized property of health information technology. NPJ Digit Med. 2020;3:9.PubMedPubMedCentralCrossRef
48.
Metadata
Title
Analysis of real-world data on growth hormone therapy adherence using a connected injection device
Authors
Ekaterina Koledova
Vincenzo Tornincasa
Paula van Dommelen
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-020-01183-1

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