scholarly journals Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey Study

10.2196/11977 ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. e11977 ◽  
Author(s):  
Jennifer Apolinário-Hagen ◽  
Mireille Menzel ◽  
Severin Hennemann ◽  
Christel Salewski
Keyword(s):  
Multiple Sclerosis ◽  
Disease Management ◽  
Mobile Health ◽  
Survey Study ◽  
Web Based ◽  
Health Apps ◽  
Mobile Health Apps

scholarly journals Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey Study (Preprint)

2018 ◽  
Author(s):  
Jennifer Apolinário-Hagen ◽  
Mireille Menzel ◽  
Severin Hennemann ◽  
Christel Salewski
Keyword(s):  
Multiple Sclerosis ◽  
Disease Management ◽  
Mobile Health ◽  
Behavioral Intentions ◽  
Self Management ◽  
Web Based ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
The Relationship ◽  
Mhealth Apps

BACKGROUND Mobile health (mHealth) apps might have the potential to promote self-management of people with multiple sclerosis (MS) in everyday life. However, the uptake of MS apps remains poor, and little is known about the facilitators and barriers for their efficient utilization, such as technology acceptance. OBJECTIVE The aim of this study was to examine the acceptance of mHealth apps for disease management in the sense of behavioral intentions to use and explore determinants of utilization among people with MS based on the Unified Theory of Acceptance and Use of Technology (UTAUT). METHODS Participants for this Web-based cross-sectional study were recruited throughout Germany with the support of regional MS associations and self-help groups. To identify determinants of intention to use MS apps, a measure based on the UTAUT was adapted with 4 key determinants (performance expectancy, effort expectancy, social influence, and facilitating conditions) and extended by Intolerance of Uncertainty (IU) and electronic health literacy. Potential influencing effects of both MS and computer self-efficacy (C-SE) as mediators and fatigue as a moderator were analyzed using Hayes’s PROCESS macro (SPSS version 3.0) for IBM SPSS version 24.0. RESULTS A total of 98 participants (mean age 47.03 years, SD 10.17; 66/98, 67% female) with moderate fatigue levels completed the survey. Although most participants (91/98, 92%) were daily smartphone users, almost two-thirds (62/98, 63%) reported no experience with MS apps. Overall, the acceptance was moderate on average (mean 3.11, SD 1.31, minimum=1 and maximum=5), with lower scores among persons with no experience (P=.04) and higher scores among current users (P<.001). In multiple regression analysis (R2=63% variance explained), performance expectancy (beta=.41) and social influence (beta=.33) were identified as significant predictors of acceptance (all P<.001). C-SE was confirmed as a partial mediator in the relationship between IU and acceptance (indirect effect: B=−.095, 95% CI −0.227 to −0.01). Furthermore, a moderated mediation by C-SE was shown in the relationship between IU and behavioral intentions to use MS apps for low (95% CI −0.42 to −0.01) and moderate levels (95% CI −0.27 to −0.01) of fatigue. CONCLUSIONS Overall, this exploratory pilot study indicates for the first time that positive expectations about the helpfulness for self-management purposes and social support might be important factors to be considered for improving the acceptance of MS apps among smartphone users with MS. However, given some inconsistent findings, especially regarding the role of effort expectancy and IU and self-efficacy, the conceptual model needs replication with a larger sample of people with MS, varying more in fatigue levels, and a longitudinal assessment of the actual usage of MS apps predicted by acceptance in the sense of behavioral intentions to use.

scholarly journals The Use of Health IT for Chronic Disease Management: A comprehensive review of diabetes applications used for self-management of the disease (Preprint)

2019 ◽  
Author(s):  
Lorrin Robinson ◽  
Jamesa Hogges ◽  
Ingrid Brown ◽  
Kennedy Craig ◽  
Akasha Lawrence ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Disease Management ◽  
Mobile Health ◽  
Chronic Disease Management ◽  
The Self ◽  
Self Management ◽  
Consumer Reviews ◽  
Health Apps ◽  
Apple Ios ◽  
Mobile Health Apps

BACKGROUND Mobile health (mHealth) smartphone applications (apps) have shown promise in the self-management of chronic disease. Management of key disease variances can be performed through these applications to increase patient engagement in disease self-management. In today’s oversaturated health app market, what selection criteria do consumers employ to choose mobile health apps for disease self-management? App quality is critical in monitoring disease controls but is often linked to consumer popularity rather clinical recommendations of effectiveness in disease management. This paper provides a comprehensive review of features found in mobile health apps frequently used in the self-management of diabetes. OBJECTIVE The objective of this study was to review features of frequently used and high consumer-rated mobile health apps used in the self-management of diabetes within the Apple iOS store. These applications were cross-referenced against high consumer-rated health apps found in other online diabetes sources. This study aimed to highlight key features of consumer-favored mobile health apps used in the self-management of diabetes. METHODS A primary Apple iOS store search was conducted using the term “diabetes apps” on an Apple iPad. The top five most frequently used mobile health apps were identified and rated by the number of consumer reviews, application ratings, and the presence of key diabetes management features: dietary blood glucose, A1C, insulin, physical activity and prescription medication. A subsequent Google search was conducted using the search term “best Apple diabetes apps”. The top three search results – Healthline, Everyday Health, and Diabetes Apps, American Diabetes Association – were explored. The top five frequently used apps among those sources were examined against the same Apple iOS criteria. RESULTS Twelve mobile health apps were reviewed in total due to repetition in popularity across the four evaluated sources. Only one health app – Glucose Buddy Diabetes Tracker – appeared most frequently used within the Apple iOS store and across the other three sources. The OneTouch Reveal app ranked first on the list in the iOS store with 39,000 consumer reviews and a rating of 4.7 out of 5.0 stars while only appearing once among the other sources. Blood glucose tracking was evident across all apps, but other disease management features varied in type with at least three of the five key features being present across the 12 reviewed apps. Subscription costs and integration needs were present which could play a major role in consumer app selection. While mobile app preference was assessed and defined by the number of consumer reviews and star ratings, there were no scientific standards used in the selection and ranking of the health apps within this study. CONCLUSIONS Mobile health applications (apps) have shown promise in chronic disease management, but a surge in development of these non-regulated health solutions points to a need for standards in quality. A governing body of health information technology, clinical, policymaking, and other industry stakeholders, including patients, could be beneficial in defining health application standards for effective chronic disease management. Variabilities in features, cost, and other management inconsistencies could be diminished by regulatory uniformity and increase both patient engagement activities and disease outcomes.

scholarly journals PNS187 US NATIONAL TRENDS OF MOBILE HEALTH APPS USE IN 2014 AND 2018

2019 ◽  
Vol 22 ◽  
pp. S317
Author(s):  
X. Feng ◽  
T. Lavelle ◽  
P.J. Lin
Keyword(s):  
Mobile Health ◽  
Health Apps ◽  
Mobile Health Apps ◽  
National Trends

scholarly journals Mobile Health Apps for Medical Emergencies: Systematic Review

10.2196/18513 ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. e18513
Author(s):  
Alejandro Plaza Roncero ◽  
Gonçalo Marques ◽  
Beatriz Sainz-De-Abajo ◽  
Francisco Martín-Rodríguez ◽  
Carlos del Pozo Vegas ◽  
...  
Keyword(s):  
Health Care ◽  
Mobile Health ◽  
Mobile Apps ◽  
Current Status ◽  
Health Technologies ◽  
Health Apps ◽  
App Store ◽  
Mobile Health Apps ◽  
Medical Emergencies ◽  
Google Play

Background Mobile health apps are used to improve the quality of health care. These apps are changing the current scenario in health care, and their numbers are increasing. Objective We wanted to perform an analysis of the current status of mobile health technologies and apps for medical emergencies. We aimed to synthesize the existing body of knowledge to provide relevant insights for this topic. Moreover, we wanted to identify common threads and gaps to support new challenging, interesting, and relevant research directions. Methods We reviewed the main relevant papers and apps available in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was used in this review. The search criteria were adopted using systematic methods to select papers and apps. On one hand, a bibliographic review was carried out in different search databases to collect papers related to each application in the health emergency field using defined criteria. On the other hand, a review of mobile apps in two virtual storage platforms (Google Play Store and Apple App Store) was carried out. The Google Play Store and Apple App Store are related to the Android and iOS operating systems, respectively. Results In the literature review, 28 papers in the field of medical emergency were included. These studies were collected and selected according to established criteria. Moreover, we proposed a taxonomy using six groups of applications. In total, 324 mobile apps were found, with 192 identified in the Google Play Store and 132 identified in the Apple App Store. Conclusions We found that all apps in the Google Play Store were free, and 73 apps in the Apple App Store were paid, with the price ranging from US $0.89 to US $5.99. Moreover, 39% (11/28) of the included studies were related to warning systems for emergency services and 21% (6/28) were associated with disaster management apps.

scholarly journals Patient-Centric Structural Determinants of Adherence Rates Among Asthma Populations: Exploring the Potential of Patient Activation and Encouragement Tool TRUSTR to Improve Adherence

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Asim Zia ◽  
Arthur Brassart ◽  
Sheila Thomas ◽  
Fen Ye ◽  
Judith Stephenson ◽  
...  
Keyword(s):  
Clinical Data ◽  
Mobile Health ◽  
Medical Condition ◽  
Total Effect ◽  
Adherence Rate ◽  
Administrative Claims ◽  
Structural Determinants ◽  
Health Apps ◽  
Mobile Health Apps ◽  
Patient Centric

Background: Lack of adherence with prescribed medications among the asthma populations exacerbates health outcomes and increases social and economic costs. Objectives: The proposed study aims to model patient-centric structural determinants of adherence rates among asthma patients and explore the potential of mobile health apps such as the TRUSTR platform to improve adherence using its power of monetary and non-monetary chatbotting and non-monetary nudges. Following specific hypotheses are tested: (1) Patient attributes, such as their age and medical condition, have significant effect on their adherence with the prescribed treatment plans. (2) Behavioral nudging with rewards and engagement via mobile health apps will increase adherence rates. Methods: The patient population (N= 37 359) consists of commercially insured patients with asthma who have been identified from administrative claims in the HealthCore Integrated Research Database (HIRD) between April 1, 2018 and March 31, 2019. Two Structural Equation Models (SEMs) are estimated to quantify direct, indirect and total effect sizes of age and medical condition on proportion of days covered (PDC) and medical possession ratio (MPR), mediated by patient medical and pharmacy visits. Fourteen additional SEMs were estimated to lateralize TRUSTR findings and conduct sensitivity analysis. Results: HIRD data reveal mean adherence rate of 59% (standard deviation (SD) 29%) for PDC and 58% for MPR (SD 36%). Key structural findings from SEMs derived from the HIRD dataset indicate that each additional year in the age of the patient has a positive total effect on the adherence rate. Patients with poor medical condition are likely to have lower adherence rate, but this direct effect is countered by mediating variables. Further, each additional reward and higher engagement with a mobile app is likely to have a positive total effect on increasing the adherence rate. Conclusions: HIRD data reveal mean adherence rate of 59% (SD 29%), providing the evidence for the opportunity to increase adherence rate by around 40%. Statistical modeling results reveal structural determinants, such as the opportunity to nudge, are higher among younger patients, as they have higher probability of being non-adherent. Methodologically, lateralization approach demonstrates the potential to capture real-world evidence beyond clinical data and merge it with clinical data.

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