High Engagement of Patients Monitored by a Digital Health Ecosystem Indicates Significant Improvements of Key r-hGH Treatment Metrics.

Digital health engagement impact on treatment real-world evidence

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
27 May 2021
Historique:
entrez: 27 5 2021
pubmed: 28 5 2021
medline: 1 6 2021
Statut: ppublish

Résumé

The early adoption of digital health solutions in the treatment of growth disorders has enabled the collection and analysis of more than 10 years of real-world data using the easypod™ connect platform. Using this rich dataset, we were able to study the impact of engagement on three key treatment-related outcomes: adherence, persistence of use, and growth. In total, data for 17,906 patients were available. The three features, regularity of injection (≤2h vs >2h), change of comfort setting (yes/no), and opting-in to receive injection reminders (yes/no), were used as a proxy for engagement. Patients were assigned to the low-engagement group (n=1,752) when all of their features had the low-engagement flag (>2h, no, no) and to the high-engagement group (n=1,081) when all of their features had the high-engagement flag (≤2h, yes, yes). The low-engagement group was down-sampled to 1,081 patients (subsample of n=37 for growth) using the iterative proportional fitting algorithm. Statistical tests were used to study the impact of engagement to the outcomes. The results show that all three outcomes were significantly improved by a factor varying from 1.8 up to 2.2 when the engagement level was high. These results should encourage the promotion of engagement and associated behaviors by both patients and healthcare professionals.

Identifiants

pubmed: 34042790
pii: SHTI210295
doi: 10.3233/SHTI210295
doi:

Types de publication

Journal Article

Langues

eng

Pagination

829-833

Auteurs

Amalia Spataru (A)

Swiss Data Science Center, ETH Zurich and EPFL, Switzerland.

Silvia Quarteroni (S)

Swiss Data Science Center, ETH Zurich and EPFL, Switzerland.

Lilian Arnaud (L)

Connected Health & Devices, Ares Trading SA, Eysins, Switzerland, an affiliate of Merck KGaA, Darmstadt, Germany.

Paula van Dommelen (P)

The Netherlands Organization for Applied Scientific Research TNO, Leiden, Netherlands.

Ekaterina Koledova (E)

Endocrinology Global Medical Affairs, Merck KGaA, Darmstadt, Germany.

Quentin Le Masne (Q)

Connected Health & Devices, Ares Trading SA, Eysins, Switzerland, an affiliate of Merck KGaA, Darmstadt, Germany.

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