The typology of digital health apps according to their quality scores and user ratings: K-Means clustering

Maciej Hyzy; Raymond Robert Bond; Maurice D Mulvenna; Lu Bai; Simon Leigh

doi:10.1145/3605655.3605786

The typology of digital health apps according to their quality scores and user ratings: K-Means clustering

Maciej Hyzy
, Raymond Robert Bond
, Maurice D Mulvenna
, Lu Bai
, Simon Leigh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This study focuses on discovering the types of digital health apps that exist in accordance with several quality characteristics and their user ratings on app stores. The quality scores include scores for the app's user experience (UX), its data privacy (DP) and professional clinical assurance (PCA) which are scores provided by ORCHA that use many objective questions to quality assess health apps (ORCHA stands for The Organisation for the Review of Care and Health Apps). K-means clustering has been used to group many digital health apps (n>1700) that have similar traits. We describe 6 different types of digital health apps. This study shows that one cluster (or type) comprise of 23.8% of health apps which typically have good user ratings and high-quality scores. Another cluster of apps comprise of 27.2% of health apps, which typically have low PCA scores but high UX and DP scores with good user ratings, indicating that this cluster of health apps are held back by their PCA score from becoming 'the highest quality' health apps.

Original language	English
Title of host publication	ECCE 2023 - Proceedings of the European Conference on Cognitive Ergonomics 2023
Editors	Alan Dix, Irene Reppa, Carina Westling
Publisher	Association for Computing Machinery
Number of pages	4
ISBN (Electronic)	9798400708756
DOIs	https://doi.org/10.1145/3605655.3605786
Publication status	Published - 21 Sept 2023
Externally published	Yes
Event	2023 European Conference on Cognitive Ergonomics, ECCE 2023 - Swansea, United Kingdom Duration: 19 Sept 2023 → 22 Sept 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2023 European Conference on Cognitive Ergonomics, ECCE 2023
Country/Territory	United Kingdom
City	Swansea
Period	19/09/2023 → 22/09/2023

Bibliographical note

Funding Information:
Funding: This study has been funded by CAST award / DfE and ORCHA.

Publisher Copyright:
© 2023 Owner/Author.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

cluster analysis
digital health apps
mHealth quality traits

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/3605655.3605786Licence: Unspecified

Cite this

Hyzy, M., Bond, R. R., Mulvenna, M. D., Bai, L., & Leigh, S. (2023). The typology of digital health apps according to their quality scores and user ratings: K-Means clustering. In A. Dix, I. Reppa, & C. Westling (Eds.), ECCE 2023 - Proceedings of the European Conference on Cognitive Ergonomics 2023 Article 20 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3605655.3605786

Hyzy, Maciej ; Bond, Raymond Robert ; Mulvenna, Maurice D et al. / The typology of digital health apps according to their quality scores and user ratings: K-Means clustering. ECCE 2023 - Proceedings of the European Conference on Cognitive Ergonomics 2023. editor / Alan Dix ; Irene Reppa ; Carina Westling. Association for Computing Machinery, 2023. (ACM International Conference Proceeding Series).

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title = "The typology of digital health apps according to their quality scores and user ratings: K-Means clustering",

abstract = "This study focuses on discovering the types of digital health apps that exist in accordance with several quality characteristics and their user ratings on app stores. The quality scores include scores for the app's user experience (UX), its data privacy (DP) and professional clinical assurance (PCA) which are scores provided by ORCHA that use many objective questions to quality assess health apps (ORCHA stands for The Organisation for the Review of Care and Health Apps). K-means clustering has been used to group many digital health apps (n>1700) that have similar traits. We describe 6 different types of digital health apps. This study shows that one cluster (or type) comprise of 23.8\% of health apps which typically have good user ratings and high-quality scores. Another cluster of apps comprise of 27.2\% of health apps, which typically have low PCA scores but high UX and DP scores with good user ratings, indicating that this cluster of health apps are held back by their PCA score from becoming 'the highest quality' health apps.",

keywords = "cluster analysis, digital health apps, mHealth quality traits",

author = "Maciej Hyzy and Bond, \{Raymond Robert\} and Mulvenna, \{Maurice D\} and Lu Bai and Simon Leigh",

note = "Funding Information: Funding: This study has been funded by CAST award / DfE and ORCHA. Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 2023 European Conference on Cognitive Ergonomics, ECCE 2023 ; Conference date: 19-09-2023 Through 22-09-2023",

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Hyzy, M, Bond, RR, Mulvenna, MD, Bai, L & Leigh, S 2023, The typology of digital health apps according to their quality scores and user ratings: K-Means clustering. in A Dix, I Reppa & C Westling (eds), ECCE 2023 - Proceedings of the European Conference on Cognitive Ergonomics 2023., 20, ACM International Conference Proceeding Series, Association for Computing Machinery, 2023 European Conference on Cognitive Ergonomics, ECCE 2023, Swansea, United Kingdom, 19/09/2023. https://doi.org/10.1145/3605655.3605786

The typology of digital health apps according to their quality scores and user ratings: K-Means clustering. / Hyzy, Maciej; Bond, Raymond Robert; Mulvenna, Maurice D et al.
ECCE 2023 - Proceedings of the European Conference on Cognitive Ergonomics 2023. ed. / Alan Dix; Irene Reppa; Carina Westling. Association for Computing Machinery, 2023. 20 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Hyzy, Maciej

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AU - Bai, Lu

AU - Leigh, Simon

PY - 2023/9/21

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AB - This study focuses on discovering the types of digital health apps that exist in accordance with several quality characteristics and their user ratings on app stores. The quality scores include scores for the app's user experience (UX), its data privacy (DP) and professional clinical assurance (PCA) which are scores provided by ORCHA that use many objective questions to quality assess health apps (ORCHA stands for The Organisation for the Review of Care and Health Apps). K-means clustering has been used to group many digital health apps (n>1700) that have similar traits. We describe 6 different types of digital health apps. This study shows that one cluster (or type) comprise of 23.8% of health apps which typically have good user ratings and high-quality scores. Another cluster of apps comprise of 27.2% of health apps, which typically have low PCA scores but high UX and DP scores with good user ratings, indicating that this cluster of health apps are held back by their PCA score from becoming 'the highest quality' health apps.

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KW - mHealth quality traits

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M3 - Conference contribution

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ER -

Hyzy M, Bond RR, Mulvenna MD, Bai L, Leigh S. The typology of digital health apps according to their quality scores and user ratings: K-Means clustering. In Dix A, Reppa I, Westling C, editors, ECCE 2023 - Proceedings of the European Conference on Cognitive Ergonomics 2023. Association for Computing Machinery. 2023. 20. (ACM International Conference Proceeding Series). doi: 10.1145/3605655.3605786

The typology of digital health apps according to their quality scores and user ratings: K-Means clustering

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this