Performance Assessment of mHealth Apps

Gerson Rodriguez; Devasena Inupakutika; Sahak Kaghyan; David Akopian; Palden Lama; Patricia Chalela; Amelie G. Ramirez

doi:10.1109/PerComWorkshops48775.2020.9156198

Performance Assessment of mHealth Apps

Gerson Rodriguez, Devasena Inupakutika, Sahak Kaghyan, David Akopian, Palden Lama, Patricia Chalela, Amelie G. Ramirez

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

3 Scopus citations

Abstract

Mobile Health (mHealth) apps are being widely used to monitor the health of patients with chronic medical conditions with the proliferation and the increasing use of smartphones. Mobile devices have limited computation power and energy supply which may lead to either delayed alarms, shorter battery life or excessive memory usage limiting their ability to execute resource-intensive functionality and inhibit proper medical monitoring. This paper presents a methodology for measurement-based performance assessment of cloud backend and mobile networks that support mHealth services. The methodology targets the assessment of a prototype mHealth app developed for breast cancer patients undergoing Endocrine Hormone Therapy (EHT). It models third-party cloud backend services to examine the performance in a representative testing scenario for end-users accessing the app. Experimental results are reported and compared for native Android and iOS implementations. The analysis further reflects the impact of the network and device battery conditions on response times and end-user quality of experience. The contribution of this work is twofold: (a) First, it presents a performance methodology and analysis of a fully functional medication adherence management mHealth app implemented on major duopoly of mobile platforms (android and iOS) and (b) Second, based on the performance analysis, conclusions are drawn that serve as the recommendation pathway for the development of similar medical reference mobile apps.

Original language	English (US)
Title of host publication	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728147161
DOIs	https://doi.org/10.1109/PerComWorkshops48775.2020.9156198
State	Published - Mar 2020
Event	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 - Austin, United States Duration: Mar 23 2020 → Mar 27 2020

Publication series

Name	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

Conference

Conference	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
Country/Territory	United States
City	Austin
Period	3/23/20 → 3/27/20

Keywords

cloud services
mobile applications
performance
smartphones

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Human-Computer Interaction
Safety, Risk, Reliability and Quality
Instrumentation

Access to Document

10.1109/PerComWorkshops48775.2020.9156198

Cite this

Rodriguez, G., Inupakutika, D., Kaghyan, S., Akopian, D., Lama, P., Chalela, P., & Ramirez, A. G. (2020). Performance Assessment of mHealth Apps. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 Article 9156198 (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PerComWorkshops48775.2020.9156198

Performance Assessment of mHealth Apps. / Rodriguez, Gerson; Inupakutika, Devasena; Kaghyan, Sahak et al.
2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9156198 (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020).

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

Rodriguez, G, Inupakutika, D, Kaghyan, S, Akopian, D, Lama, P, Chalela, P & Ramirez, AG 2020, Performance Assessment of mHealth Apps. in 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020., 9156198, 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, United States, 3/23/20. https://doi.org/10.1109/PerComWorkshops48775.2020.9156198

Rodriguez G, Inupakutika D, Kaghyan S, Akopian D, Lama P, Chalela P et al. Performance Assessment of mHealth Apps. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9156198. (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020). doi: 10.1109/PerComWorkshops48775.2020.9156198

Rodriguez, Gerson ; Inupakutika, Devasena ; Kaghyan, Sahak et al. / Performance Assessment of mHealth Apps. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020).

@inproceedings{2a1d5e20b0b84fdba629d605599f6e32,

title = "Performance Assessment of mHealth Apps",

abstract = "Mobile Health (mHealth) apps are being widely used to monitor the health of patients with chronic medical conditions with the proliferation and the increasing use of smartphones. Mobile devices have limited computation power and energy supply which may lead to either delayed alarms, shorter battery life or excessive memory usage limiting their ability to execute resource-intensive functionality and inhibit proper medical monitoring. This paper presents a methodology for measurement-based performance assessment of cloud backend and mobile networks that support mHealth services. The methodology targets the assessment of a prototype mHealth app developed for breast cancer patients undergoing Endocrine Hormone Therapy (EHT). It models third-party cloud backend services to examine the performance in a representative testing scenario for end-users accessing the app. Experimental results are reported and compared for native Android and iOS implementations. The analysis further reflects the impact of the network and device battery conditions on response times and end-user quality of experience. The contribution of this work is twofold: (a) First, it presents a performance methodology and analysis of a fully functional medication adherence management mHealth app implemented on major duopoly of mobile platforms (android and iOS) and (b) Second, based on the performance analysis, conclusions are drawn that serve as the recommendation pathway for the development of similar medical reference mobile apps. ",

keywords = "cloud services, mobile applications, performance, smartphones",

author = "Gerson Rodriguez and Devasena Inupakutika and Sahak Kaghyan and David Akopian and Palden Lama and Patricia Chalela and Ramirez, {Amelie G.}",

note = "Funding Information: The study is supported in part by Susan G. Komen (Award No. SAB160005), the Mays Cancer Center (Grant No. P30 CA054174) and Redes En Acci{\'o}n (Grant No. U54 CA153511). Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 ; Conference date: 23-03-2020 Through 27-03-2020",

year = "2020",

month = mar,

doi = "10.1109/PerComWorkshops48775.2020.9156198",

language = "English (US)",

series = "2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020",

address = "United States",

}

TY - GEN

T1 - Performance Assessment of mHealth Apps

AU - Rodriguez, Gerson

AU - Inupakutika, Devasena

AU - Kaghyan, Sahak

AU - Akopian, David

AU - Lama, Palden

AU - Chalela, Patricia

AU - Ramirez, Amelie G.

N1 - Funding Information: The study is supported in part by Susan G. Komen (Award No. SAB160005), the Mays Cancer Center (Grant No. P30 CA054174) and Redes En Acción (Grant No. U54 CA153511). Publisher Copyright: © 2020 IEEE.

PY - 2020/3

Y1 - 2020/3

N2 - Mobile Health (mHealth) apps are being widely used to monitor the health of patients with chronic medical conditions with the proliferation and the increasing use of smartphones. Mobile devices have limited computation power and energy supply which may lead to either delayed alarms, shorter battery life or excessive memory usage limiting their ability to execute resource-intensive functionality and inhibit proper medical monitoring. This paper presents a methodology for measurement-based performance assessment of cloud backend and mobile networks that support mHealth services. The methodology targets the assessment of a prototype mHealth app developed for breast cancer patients undergoing Endocrine Hormone Therapy (EHT). It models third-party cloud backend services to examine the performance in a representative testing scenario for end-users accessing the app. Experimental results are reported and compared for native Android and iOS implementations. The analysis further reflects the impact of the network and device battery conditions on response times and end-user quality of experience. The contribution of this work is twofold: (a) First, it presents a performance methodology and analysis of a fully functional medication adherence management mHealth app implemented on major duopoly of mobile platforms (android and iOS) and (b) Second, based on the performance analysis, conclusions are drawn that serve as the recommendation pathway for the development of similar medical reference mobile apps.

AB - Mobile Health (mHealth) apps are being widely used to monitor the health of patients with chronic medical conditions with the proliferation and the increasing use of smartphones. Mobile devices have limited computation power and energy supply which may lead to either delayed alarms, shorter battery life or excessive memory usage limiting their ability to execute resource-intensive functionality and inhibit proper medical monitoring. This paper presents a methodology for measurement-based performance assessment of cloud backend and mobile networks that support mHealth services. The methodology targets the assessment of a prototype mHealth app developed for breast cancer patients undergoing Endocrine Hormone Therapy (EHT). It models third-party cloud backend services to examine the performance in a representative testing scenario for end-users accessing the app. Experimental results are reported and compared for native Android and iOS implementations. The analysis further reflects the impact of the network and device battery conditions on response times and end-user quality of experience. The contribution of this work is twofold: (a) First, it presents a performance methodology and analysis of a fully functional medication adherence management mHealth app implemented on major duopoly of mobile platforms (android and iOS) and (b) Second, based on the performance analysis, conclusions are drawn that serve as the recommendation pathway for the development of similar medical reference mobile apps.

KW - cloud services

KW - mobile applications

KW - performance

KW - smartphones

UR - http://www.scopus.com/inward/record.url?scp=85091966286&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85091966286&partnerID=8YFLogxK

U2 - 10.1109/PerComWorkshops48775.2020.9156198

DO - 10.1109/PerComWorkshops48775.2020.9156198

M3 - Conference contribution

AN - SCOPUS:85091966286

T3 - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

BT - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

Y2 - 23 March 2020 through 27 March 2020

ER -

Performance Assessment of mHealth Apps

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this