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Open accessJournal ArticleDOI: 10.1080/14737167.2021.1891883

Distinguishing features in the assessment of mHealth apps.

04 Mar 2021-Expert Review of Pharmacoeconomics & Outcomes Research (Expert Rev Pharmacoecon Outcomes Res)-Vol. 21, Iss: 4, pp 521-526
Abstract: Introduction: The unparalleled surge in digital health adoption during the COVID-19 pandemic has emphasized the potential of mHealth apps. However, the quality of available evidence is generally low, and regulatory frameworks have focused on apps with medical purposes only, overlooking apps with significant interactions with patients that may require stronger oversight.Areas covered: To support this expanded evidence generation process, we identified the reasons that distinguish mHealth apps compared to medical devices at large and that should differentially feature their assessment. mHealth apps are characterized by the iterative nature of the corresponding interventions, frequent user interactions with a non-linear relationship between technology usage, engagement and outcomes, significant organizational implications, as well as challenges associated with genericization, their broad diagnostic potential, and price setting.Expert Opinion: The renewed reliance experienced during the pandemic and the unprecedented injection of resources through recovery instruments can further boost the development of apps. Only robust evidence of the benefits of mHealth apps will persuade health-care professionals and beneficiaries to systematically deploy them. Regulatory bodies will need to question their current approaches by adopting comprehensive evaluation processes that adequately consider the specific features of mHealth apps.

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Topics: mHealth (61%), Digital health (52%)

7 results found

Open accessJournal ArticleDOI: 10.3390/J4020017
16 Jun 2021-
Abstract: Within the healthcare environment, mobile health (mHealth) applications (apps) are becoming more and more important. The number of new mHealth apps has risen steadily in the last years. Especially the COVID-19 pandemic has led to an enormous amount of app releases. In most countries, mHealth applications have to be compliant with several regulatory aspects to be declared a “medical app”. However, the latest applicable medical device regulation (MDR) does not provide more details on the requirements for mHealth applications. When developing a medical app, it is essential that all contributors in an interdisciplinary team—especially software engineers—are aware of the specific regulatory requirements beforehand. The development process, however, should not be stalled due to integration of the MDR. Therefore, a developing framework that includes these aspects is required to facilitate a reliable and quick development process. The paper at hand introduces the creation of such a framework on the basis of the Corona Health and Corona Check apps. The relevant regulatory guidelines are listed and summarized as a guidance for medical app developments during the pandemic and beyond. In particular, the important stages and challenges faced that emerged during the entire development process are highlighted.

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Topics: mHealth (61%), Medical software (50%)

2 Citations

Open accessJournal ArticleDOI: 10.3390/IJERPH18126509
Abstract: The aim of the study was to estimate the level of the human resources index (HRI) measure among Swedish municipal employees, and to investigate the association between human resources index (HRI) and relational justice, short-term recovery, work environment-related production loss, and health-related production loss. A cross-sectional design was used with one sample of municipal employees (n = 6402). The results showed a positive association (r = 0.31) between human resources index (HRI) and relational justice; a positive (r = 0.27) association between HRI and short-term recovery; a negative association between HRI and work environment-related production loss (r = −0.37); and a negative association between HRI and health-related production loss (r = −0.23). The findings implicate that HRI captures important aspects of the work environment such as productivity, relational justice, and short-term recovery. The HRI measure is part of a support model used in workplaces to systematically address work environment-related issues. Monitoring changes in the HRI measure, it is possible to determine whether the measures taken effect production loss, perceived leadership, and short-term recovery in a work group. The support model using HRI may thus be used to complement traditional work environment surveys conducted in Swedish organizations as obliged by legal provisions.

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1 Citations

Open accessJournal ArticleDOI: 10.2196/25071
01 Dec 2021-
Abstract: Background: There is currently limited evidence on the level and intensity of physical activity in individuals with hemophilia A. Mobile technologies can offer a rigorous and reliable alternative to support data collection processes but they are often associated with poor user retention. The lack of longitudinal continuity in their use can be partly attributed to the insufficient consideration of stakeholder inputs in the development process of mobile apps. Several user-centered models have been proposed to guarantee that a thorough knowledge of the end user needs is considered in the development process of mobile apps. Objective: The aim of this study is to design and validate an electronic patient-reported outcome mobile app that requires sustained active input by individuals during POWER, an observational study that aims at evaluating the relationship between physical activity levels and bleeding in patients with hemophilia A. Methods: We adopted a user-centered design and engaged several stakeholders in the development and usability testing of this mobile app. During the concept generation and ideation phase, we organized a need-assessment focus group (FG) with patient representatives to elicit specific design requirements for the end users. We then conducted 2 exploratory FGs to seek additional inputs for the app’s improvement and 2 confirmatory FGs to validate the app and test its usability in the field through the mobile health app usability questionnaire. Results: The findings from the thematic analysis of the need-assessment FG revealed that there was a demand for sense making, for simplification of app functionalities, for maximizing integration, and for minimizing the feeling of external control. Participants involved in the later stages of the design refinement contributed to improving the design further by upgrading the app’s layout and making the experience with the app more efficient through functions such as chatbots and visual feedback on the number of hours a wearable device had been worn, to ensure that the observed data were actually registered. The end users rated the app highly during the quantitative assessment, with an average mobile health app usability questionnaire score of 5.32 (SD 0.66; range 4.44-6.23) and 6.20 (SD 0.43; range 5.72-6.88) out of 7 in the 2 iterative usability testing cycles. Conclusions: The results of the usability test indicated a high, growing satisfaction with the electronic patient-reported outcome app. The adoption of a thorough user-centered design process using several types of FGs helped maximize the likelihood of sustained retention of the app’s users and made it fit for data collection of relevant outcomes in the observational POWER study. The continuous use of the app and the actual level of engagement will be evaluated during the ongoing trial. Trial Registration: NCT04165135;

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Topics: Usability (60%), Mobile technology (54%), User-centered design (53%) ... show more

1 Citations

Open accessJournal ArticleDOI: 10.2147/CEOR.S334274
Abstract: Background A prescription digital therapeutic (PDT) (reSET-O®) may expand access to behavioral treatment for patients with opioid use disorder (OUD) treated with buprenorphine, but long-term data on effectiveness are lacking. Objective To compare real-world healthcare resource utilization (HCRU) among patients who engaged with reSET-O and buprenorphine compared to similar patients in recovery treated with buprenorphine who did not fill their reSET-O script or engage with the PDT beyond week one. Methods A retrospective analysis of facility and clinical service claims data was conducted in adults with PDT initiation and between 12 weeks and 9 months of continuous enrollment in a health plan after initiation. Patients who filled their prescription and engaged with the therapeutic were compared to patients who filled the prescription but did not engage beyond week one (NE), and patients who did not fill the prescription (NR) (the latter two groups combined into one group hereafter referred to as "non-engagers"). Comparisons were analyzed using a repeated-measures negative binomial model of encounters/procedures, adjusted for number of days in each period. Associated cost trends assessed using current Medicare reimbursement rates. Results A total of 444 patients redeemed a prescription and engaged with the PDT (mean age 37.5 years, 63.1% female, 84% Medicaid), and 64 patients did not engage with the PDT (mean age 39.5 years, 32.8% female, 73.4% Medicaid). Total cost of hospital facility encounters was $2693 for engaged patients vs $6130 for non-engaged patients. Engaged patients had somewhat higher rates of certain clinician services. Total facility and clinician services costs for engaged vs non-engaged patients were $8733 vs $11,441, for a net cost savings over 9 months of $2708 per patient who engaged with reSET-O. Conclusion Patients who engaged with an OUD-specific PDT had a net cost reduction for inpatient and outpatient services of $2708 per patient over 9 months compared to patients who did not engage with the PDT, despite similar levels of buprenorphine adherence.

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Topics: Medical prescription (51%), Buprenorphine (51%)

Open accessJournal ArticleDOI: 10.1038/S41746-021-00517-1
05 Oct 2021-
Abstract: COVID-19 pandemic challenges have accelerated the reliance on digital health fuelling the expanded incorporation of mobile apps into healthcare services, particularly for the management of long-term conditions such as chronic diseases (CDs). However, the impact of health apps on outcomes for CD remains unclear, potentially owing to both the poor adoption of formal development standards in the design process and the methodological quality of studies. A systematic search of randomised trials was performed on Medline, ScienceDirect, the Cochrane Library and Scopus to provide a comprehensive outlook and review the impact of health apps on CD. We identified 69 studies on diabetes (n = 29), cardiovascular diseases (n = 13), chronic respiratory diseases (n = 13), cancer (n = 10) or their combinations (n = 4). The apps rarely adopted developmental factors in the design stage, with only around one-third of studies reporting user or healthcare professional engagement. Apps differed significantly in content, with a median of eight behaviour change techniques adopted, most frequently pertaining to the 'Feedback and monitoring' (91%) and 'Shaping knowledge' (72%) categories. As for the study methodologies, all studies adopted a traditional randomised control trial (RCT) design, with relatively short follow-ups and limited sample sizes. Findings were not significant for the majority of studies across all CD, with most RCTs revealing a high risk of bias. To support the adoption of apps for CD management, this review reinforces the need for more robust development and appropriate study characteristics to sustain evidence generation and elucidate whether study results reflect the true benefits of apps or a biased estimate due to unsuitable designs.

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Topics: Cochrane Library (51%), Digital health (51%)


36 results found

Open accessJournal ArticleDOI: 10.2196/JMIR.7.1.E11
Gunther Eysenbach1Institutions (1)
Abstract: In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users. [J Med Internet Res 2005;7(1):e11]

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Topics: eHealth (60%), Population (53%), Attrition (52%)

1,792 Citations

Open accessJournal ArticleDOI: 10.1016/J.AMEPRE.2013.03.017
Santosh Kumar1, Wendy Nilsen2, Amy P. Abernethy3, Audie A. Atienza2  +15 moreInstitutions (14)
Abstract: Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

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Topics: mHealth (69%), Health technology (51%)

676 Citations

Open accessJournal ArticleDOI: 10.2337/DC11-0366
01 Sep 2011-Diabetes Care
Abstract: OBJECTIVE To test whether adding mobile application coaching and patient/provider web portals to community primary care compared with standard diabetes management would reduce glycated hemoglobin levels in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS A cluster-randomized clinical trial, the Mobile Diabetes Intervention Study, randomly assigned 26 primary care practices to one of three stepped treatment groups or a control group (usual care). A total of 163 patients were enrolled and included in analysis. The primary outcome was change in glycated hemoglobin levels over a 1-year treatment period. Secondary outcomes were changes in patient-reported diabetes symptoms, diabetes distress, depression, and other clinical (blood pressure) and laboratory (lipid) values. Maximal treatment was a mobile- and web-based self-management patient coaching system and provider decision support. Patients received automated, real-time educational and behavioral messaging in response to individually analyzed blood glucose values, diabetes medications, and lifestyle behaviors communicated by mobile phone. Providers received quarterly reports summarizing patient’s glycemic control, diabetes medication management, lifestyle behaviors, and evidence-based treatment options. RESULTS The mean declines in glycated hemoglobin were 1.9% in the maximal treatment group and 0.7% in the usual care group, a difference of 1.2% ( P = 0.001) over 12 months. Appreciable differences were not observed between groups for patient-reported diabetes distress, depression, diabetes symptoms, or blood pressure and lipid levels (all P > 0.05). CONCLUSIONS The combination of behavioral mobile coaching with blood glucose data, lifestyle behaviors, and patient self-management data individually analyzed and presented with evidence-based guidelines to providers substantially reduced glycated hemoglobin levels over 1 year.

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Topics: Glycated hemoglobin (66%), Diabetes management (62%), Type 2 diabetes (56%) ... show more

493 Citations

Journal ArticleDOI: 10.1089/DIA.2008.0283
Abstract: Background: Less than 63% of individuals with diabetes meet professional guidelines target of hemoglobin A1c <7.0%, and only 7% meet combined glycemic, lipid, and blood pressure goals. The primary study aim was to assess the impact on A1c of a cell phone-based diabetes management software system used with web-based data analytics and therapy optimization tools. Secondary aims examined health care provider (HCP) adherence to prescribing guidelines and assessed HCPs' adoption of the technology. Methods: Thirty patients with type 2 diabetes were recruited from three community physician practices for a 3-month study and evenly randomized. The intervention group received cell phone-based software designed by endocrinologists and CDEs (WellDoc™ Communications, Inc., Baltimore, MD). The software provided real-time feedback on patients' blood glucose levels, displayed patients' medication regimens, incorporated hypo- and hyperglycemia treatment algorithms, and requested additional data needed to evaluate...

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Topics: Diabetes management (59%), Patient satisfaction (54%), Randomized controlled trial (52%) ... show more

383 Citations

Open accessJournal ArticleDOI: 10.2196/JMIR.7126
Susan Michie1, Lucy Yardley2, Robert West1, Kevin Patrick3  +1 moreInstitutions (4)
Abstract: Devices and programs using digital technology to foster or support behavior change (digital interventions) are increasingly ubiquitous, being adopted for use in patient diagnosis and treatment, self-management of chronic diseases, and in primary prevention. They have been heralded as potentially revolutionizing the ways in which individuals can monitor and improve their health behaviors and health care by improving outcomes, reducing costs, and improving the patient experience. However, we are still mainly in the age of promise rather than delivery. Developing and evaluating these digital interventions presents new challenges and new versions of old challenges that require use of improved and perhaps entirely new methods for research and evaluation. This article discusses these challenges and provides recommendations aimed at accelerating the rate of progress in digital behavior intervention research and practice. Areas addressed include intervention development in a rapidly changing technological landscape, promoting user engagement, advancing the underpinning science and theory, evaluating effectiveness and cost-effectiveness, and addressing issues of regulatory, ethical, and information governance. This article is the result of a two-day international workshop on how to create, evaluate, and implement effective digital interventions in relation to health behaviors. It was held in London in September 2015 and was supported by the United Kingdom's Medical Research Council (MRC), the National Institute for Health Research (NIHR), the Methodology Research Programme (PI Susan Michie), and the Robert Wood Johnson Foundation of the United States (PI Kevin Patrick). Important recommendations to manage the rapid pace of change include considering using emerging techniques from data science, machine learning, and Bayesian approaches and learning from other disciplines including computer science and engineering. With regard to assessing and promoting engagement, a key conclusion was that sustained engagement is not always required and that for each intervention it is useful to establish what constitutes "effective engagement," that is, sufficient engagement to achieve the intended outcomes. The potential of digital interventions for testing and advancing theories of behavior change by generating ecologically valid, real-time objective data was recognized. Evaluations should include all phases of the development cycle, designed for generalizability, and consider new experimental designs to make the best use of rich data streams. Future health economics analyses need to recognize and model the complex and potentially far-reaching costs and benefits of digital interventions. In terms of governance, developers of digital behavior interventions should comply with existing regulatory frameworks, but with consideration for emerging standards around information governance, ethics, and interoperability.

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Topics: Behavior change (58%), Information governance (56%), Health informatics (55%) ... show more

342 Citations

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