Exploring mHealth monitoring service acceptance from a service characteristics perspective

Wang, Liuan; Wu, Tianshi; Guo, Xitong; Zhang, Xiaofei; Li, Yan; Wang, Weiguo

doi:10.1016/j.elerap.2018.06.003

Cited by 24 publications

(13 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One reason for the dropouts in this study may be suboptimal service matching, that is, the users engage with the app but experience that the app may not fit their exact needs [ 38 ]. The C4C app offers modules targeting different aspects of diabetes treatment.…”

Section: Discussionmentioning

confidence: 99%

Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study

Böhm¹,

Jensen²,

Sørensen³

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Patient support apps have risen in popularity and provide novel opportunities for self-management of diabetes. Such apps offer patients to play an active role in monitoring their condition, thereby increasing their own treatment responsibility. Although many health apps require active user engagement to be effective, there is little evidence exploring engagement with mobile health (mHealth). Objective This study aims to analyze the extent to which users engage with mHealth for diabetes and identify patient characteristics that are associated with engagement. Methods The analysis is based on real-world data obtained by Novo Nordisk’s Cornerstones4Care Powered by Glooko diabetes support app. User engagement was assessed as the number of active days and using measures expressing the persistence, longevity, and regularity of interaction within the first 180 days of use. Beta regressions were estimated to assess the associations between user characteristics and engagement outcomes for each module of the app. Results A total of 9051 individuals initiated use after registration and could be observed for 180 days. Among these, 55.39% (5013/9051) used the app for one specific purpose. The average user activity ratio varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. Average user engagement was lower if modules required manual data entries, although the initial uptake was higher for these modules. Regression analyses further revealed that although more women used the app (2075/3649, 56.86%), they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively. Conclusions Strategies to increase or sustain the use of apps and availability of health data may target the mode of data collection and content design and should take into account privacy concerns of the users at the same time. Users’ engagement was determined by various user characteristics, indicating that particular patient groups should be targeted or assisted when integrating apps into the self-management of their disease.

show abstract

Section: Discussionmentioning

confidence: 99%

Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study

Böhm¹,

Jensen²,

Sørensen³

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…A substantial number of researches on mHealth/eHealth have been rising explosively in the last few years due to acknowledgement of the value of wireless technologies for healthcare delivery, explosive mobile phone penetration rate and massive funding initiatives in mHealth/eHealth infrastructure (Cameron et al, 2017, Wang et al, 2018.…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach

Alam

Kaium

et al. 2020

Technology in Society

132

100

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

“…The OHC has developed very rapidly in China [ 8 , 9 ]. Up to 194.8 million people used online health care services in December 2016, accounting for 26.6% of the total number of internet users [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Consultation Pricing of the Online Health Care Service in China: Hierarchical Linear Regression Approach

Chiu¹,

Wang²,

Yu³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background Online health care services are a possible solution to alleviate the lack of medical resources in rural areas, and further understanding of the related medical service pricing system would contribute to improvement of the online health care community (OHC). Although many studies have investigated the OHC, the impact of physicians’ reputations and wage levels on consulting prices in the OHC has rarely been discussed in the literature. Objective This study was designed to explore the determinants of consulting prices in the OHC. We addressed the following questions: (1) Are the prices of online health consultation services affected by wage levels at the doctor’s location? (2) How does a physician’s online and offline reputation affect their consulting prices? Methods Employing a large-scale sample of 16,008 doctors in China, we first used descriptive statistics to investigate the determinants of consulting prices in their entirety. Hierarchical linear modeling was then used to investigate the determinants of consulting prices in the OHC. Results The empirical results led to the conclusion that if doctors have more elevated clinic titles, work in higher-level hospitals, have better online reputations, and/or have made more past sales, their consulting prices will be higher. Additionally, the wage level in the city in which the doctor is working determines their opportunity cost and therefore also affects consulting prices. Conclusions The findings indicate that the characteristics of the doctor, the doctor’s online reputation, and past sales affect the consulting price. In particular, the wage level in the city affects the price of the consultation. These findings highlight that the OHC is important because it can indeed break through geographical restrictions and give rural residents the opportunity to obtain medical service from doctors in big cities. However, doctors from cities often charge higher fees because of their higher opportunity cost. The results reveal that one of the most important functions of the OHC is to reduce the medical disparity between urban and rural areas; however, planners appear to ignore the possibility that rural residents with lower incomes may not be able to afford such high medical consultation costs. Therefore, the government should consider providing incentives to encourage urban doctors to provide discounts to rural residents or directly offer appropriate subsidies.

show abstract

Exploring mHealth monitoring service acceptance from a service characteristics perspective

Cited by 24 publications

References 76 publications

Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study

Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study

Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach

Consultation Pricing of the Online Health Care Service in China: Hierarchical Linear Regression Approach

Contact Info

Product

Resources

About