Breast cancer is the most common cancer in women both in developed and developing countries. More than half of all cancer mobile application concern breast cancer. Gamification is widely used in mobile software applications created for health-related services. Current prevalence of gamification in breast cancer apps is unknown and detection must be manually performed. The purpose of this study is to describe and produce a tool allowing automatic detection of apps which contain gamification elements and thus empowering researchers to study gamification using large data samples. Predictive logistic regression model was designed on data extracted from breast cancer apps title and description text available in app stores. Model was validated comparing estimated and benchmark values, observed by gamification specialists. Studys outcome can be applied as a screening tool to efficiently identify gamification presence in breast cancer apps for further research.
|Title of host publication||Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017|
|Editors||Panagiotis D. Bamidis, Stathis Th. Konstantinidis, Pedro Pereira Rodrigues|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||6|
|Publication status||Published - 10 Nov 2017|
|Event||30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 - Thessaloniki, Greece|
Duration: 22 Jun 2017 → 24 Jun 2017
|Name||Proceedings - IEEE Symposium on Computer-Based Medical Systems|
|Conference||30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017|
|Period||22/06/17 → 24/06/17|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT Guido Giunti gratefully acknowledges the grant number 676201 for the Connected Health Early Stage researcher Support System (CHESS ITN) from the Horizon 2020 Program of the European Commission.
© 2017 IEEE.
Copyright 2018 Elsevier B.V., All rights reserved.
- breast cancer
- health apps
- medical apps
- medical informatics
- predictive models