By the numbers: ratings and utilization of behavioral health mobile applications.
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ABSTRACT:
SUBMITTER: Carlo AD
PROVIDER: S-EPMC6572775 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
By the numbers: ratings and utilization of behavioral health mobile applications.
Carlo Andrew D AD Hosseini Ghomi Reza R Renn Brenna N BN Areán Patricia A PA
NPJ digital medicine 20190617
Although >10,000 behavioral health applications ("apps") are currently available on the Apple and Google Play marketplaces, they have been minimally evaluated or regulated and little is known about "real world" usage patterns. This investigation combined data from online behavioral health app rating frameworks and a mobile health market research firm to identify the most downloaded apps as well as determine rating and ranking concordance between frameworks. Findings demonstrated that the most co ...[more]
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Project description:BackgroundMobile health applications are being increasingly used for people's health management. The different uses of mobile health applications lead to different health outcomes. Although active usage of mobile health applications is shown to be linked to the effectiveness of mobile health services, the factors that influence people's active usage of mobile health applications are not well studied.ObjectiveThis paper aims to examine the antecedents of active usage of mobile health applications.MethodsGrounded on the 3-factor theory, we proposed 10 attributes of mobile health applications that influence the active usage of mobile health applications through consumers' satisfaction and dissatisfaction. We classified these 10 attributes into 3 categories (ie, excitement attributes, performance attributes, and basic attributes). Using the survey method, 494 valid responses were collected and analyzed using structural equation modeling.ResultsOur analysis results revealed that both consumer satisfaction (β=0.351, t=6.299, P<.001) and dissatisfaction (β=-0.251, t=5.119, P<.001) significantly influenced active usage. With regard to the effect of attributes, excitement attributes (β=0.525, t=12.861, P<.001) and performance attributes (β=0.297, t=6.508, P<.001) positively influenced consumer satisfaction, while performance attributes (β=-0.231, t=3.729, P<.001) and basic attributes (β=-0.412, t=7.132, P<.001) negatively influenced consumer dissatisfaction. The results of the analysis confirmed our proposed hypotheses.ConclusionsOur study provides a novel perspective to study the active usage of mobile health applications. By categorizing the attributes of mobile health applications into 3 categories, the differential effects of different attributes can be tested. Meanwhile, consumer satisfaction and dissatisfaction are confirmed to be independent from each other.
| S-EPMC8734924 | biostudies-literature
Project description:BackgroundMobile health (mHealth) apps have gained significant popularity over the last few years due to their tremendous benefits, such as lowering health care costs and increasing patient awareness. However, the sensitivity of health care data makes the security of mHealth apps a serious concern. Poor security practices and lack of security knowledge on the developers' side can cause several vulnerabilities in mHealth apps.ObjectiveIn this review paper, we aimed to identify and analyze the reported challenges concerning security that developers of mHealth apps face. Additionally, our study aimed to develop a conceptual framework with the challenges for developing secure apps faced by mHealth app development organizations. The knowledge of such challenges can help to reduce the risk of developing insecure mHealth apps.MethodsWe followed the systematic literature review method for this review. We selected studies that were published between January 2008 and October 2020 since the major app stores launched in 2008. We selected 32 primary studies using predefined criteria and used a thematic analysis method for analyzing the extracted data.ResultsOf the 1867 articles obtained, 32 were included in this review based on the predefined criteria. We identified 9 challenges that can affect the development of secure mHealth apps. These challenges include lack of security guidelines and regulations for developing secure mHealth apps (20/32, 63%), developers' lack of knowledge and expertise for secure mHealth app development (18/32, 56%), lack of stakeholders' involvement during mHealth app development (6/32, 19%), no/little developer attention towards the security of mHealth apps (5/32, 16%), lack of resources for developing a secure mHealth app (4/32, 13%), project constraints during the mHealth app development process (4/32, 13%), lack of security testing during mHealth app development (4/32, 13%), developers' lack of motivation and ethical considerations (3/32, 9%), and lack of security experts' engagement during mHealth app development (2/32, 6%). Based on our analysis, we have presented a conceptual framework that highlights the correlation between the identified challenges.ConclusionsWhile mHealth app development organizations might overlook security, we conclude that our findings can help them to identify the weaknesses and improve their security practices. Similarly, mHealth app developers can identify the challenges they face to develop mHealth apps that do not pose security risks for users. Our review is a step towards providing insights into the development of secure mHealth apps. Our proposed conceptual framework can act as a practice guideline for practitioners to enhance secure mHealth app development.
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Project description:BackgroundMobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians' participation in mHealth apps.ObjectiveThis study aims to empirically explore factors affecting physicians' usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings.MethodsWe collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians' usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias.ResultsOur results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians' cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention.ConclusionsThis study contributes to the existing literature on UTAUT2 extension of physicians' acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians' usage behaviors on mHealth apps in China and provide such apps' managers with an insight into the promotion of physicians' active acceptance and usage behaviors.
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Project description:OBJECTIVE:Examine how behavioral health (BH) integration affects health care costs, emergency department (ED) visits, and inpatient admissions. DATA SOURCES/STUDY SETTING:Truven Health MarketScan Research Databases. STUDY DESIGN:Social network analysis identified "care communities" (providers sharing a high number of patients) and measured BH integration in terms of how connected, or central, BH providers were to other providers in their community. Multivariable generalized linear models adjusting for age, sex, number of prescriptions, and Charlson comorbidity score were used to estimate the relationship between the centrality of BH providers and health care utilization of BH patients. DATA COLLECTION/EXTRACTION METHODS:Used outpatient, inpatient, and pharmacy claims data from six Medicaid plans from 2011 to 2013 to identify study outcomes, comorbidities, providers, and health care encounters. PRINCIPAL FINDINGS:Behavioral health centrality ranged from 0 (no BH providers) to 0.49. Relative to communities at the median BH centrality (0.06), in 2012, BH patients in communities at the 75th percentile of BH centrality (0.31) had 0.2 fewer admissions, 2.1 fewer all-cause ED visits, and accrued $1,947 fewer costs, on average. CONCLUSIONS:Increased behavioral centrality was significantly associated with a reduced number of ED visits, less frequent inpatient admissions, and lower overall health care costs.
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