mHealth Archives - Healthentia

SaMD & mHealth apps: Status and promising results for improvement of health outcomes

System — Fri, 24 May 2024 11:16:35 +0000

Software as a Medical Devices (SaMD) and mobile health (mHealth) applications have revolutionized the healthcare landscape in the areas of Remote Patient Monitoring (RPM) and Digital Therapeutics (DTx). These technological advancements offer a range of benefits, from improved patient engagement and real-time monitoring to personalized treatment plans and enhanced clinical outcomes. During the continuous design, development, and validation of our medical device Healthentia, we have conducted a comprehensive systematic literature review to assess the current status and efficacy of these digital health interventions and benchmark the results with our finding from the clinical evidence we have collected from various studies. The results are rather positive, demonstrating how SaMDs like Healthentia can support the management of chronic diseases, satisfying patient safety and performance requirements.

The current landscape of SaMD and mHealth apps is characterized by rapid innovation and widespread adoption, especially after the Covid pandemic, which might have worked as a catalyst for digital transformation in digital health. SaMD refers to software intended to be used for medical purposes that perform these purposes without being part of a hardware medical device. mHealth applications, on the other hand, encompass a wide range of digital tools designed to support health and wellness through mobile devices. mHealth applications are not necessarily medical devices and therefore, they often don’t comply with the regulatory framework. Both SaMD and mHealth apps have seen significant advancements, driven by the increasing need for accessible, efficient, and patient-centric healthcare solutions. Regulatory frameworks have also evolved to keep pace with these innovations, ensuring that these technologies meet rigorous safety and efficacy standards. While the potential of SaMD and mHealth apps are immense, there are several challenges that need to be addressed to fully realize their benefits, such as data privacy concerns, integration with existing healthcare systems, and user adoption barriers.

The review involved a scientific process of identifying, selecting, and analyzing relevant studies that evaluate the effectiveness of SaMD and mHealth apps in managing chronic diseases, using the PICO (Population, Intervention, Comparison, Outcomes and Study) framework. The selected studies provide a robust evidence base, highlighting the positive impact of these technologies on patient outcomes.

This post aims to provide a comprehensive overview of the current status of SaMD and mHealth apps, highlight promising results, and discuss the potential these technologies hold for improving health outcomes and provides only an overview of a peer reviewed paper that we expect to publish in the coming months.

The safety and performances of such solutions for chronic disease patients is thoroughly discussed in various meta-analyses that have been retrieved and analyzed and include 14 meta-analyses for diabetes Type II, cardiovascular diseases, COPD and cancer. Those meta-analyses allow to identify the main clinical performances that could be expected for Healthentia to define the acceptance criteria for the risks/benefits acceptability. In addition, clinical studies originating from the selected systematic reviews were analyzed to check which features were put in place in the eHealth solutions under evaluation, allowing a comparison with those of Healthentia, resulting in prospective controlled studies on several chronic diseases:

Diabetes Type II: 24 studies, about 1.200 patients in the intervention group
COPD: 1 study, 36 patients
Cancer: 3 studies, about 150 patients
Heart diseases: 7 studies, 560 patients

Summarizing the results from these studies we provide some data both biomarkers and other clinical endpoints that demonstrate the potential:

HbA1c level: Decreased in several of the studies.
Physical activity & performance: Significant improvements in exercise capacity.
Body fat: Significant improvements.
Sleep: Significant improvements in sleep time.
Self-care/management: Self-reported improved the self-monitoring of patients’ blood glucose levels, diet, exercise, other self-management skills, and knowledge of the disease.
Depression & anxiety: Self-reported improved effect on their psychological status.
6MWD: Significant improvement.
(HR)QoL: Difference between the DQoL–Social/Vocational Concerns subscale scores was statistically significant.
Hospital readmissions: The 30-day hospital readmission rate was much lower, compared with the national readmission rates.
Adherence: Significant improved medication adherence.

The list of results above is indicative and is only provided to showcase the potential and is not to be considered as a scientific outcome itself. Scientific results will be presented and analyzed in the Systematic Literature Review paper in the coming months, which will also include evidence and findings from studies that used Healthentia. By sharing our findings and experiences, we hope to contribute to the ongoing dialogue on digital health innovations and their role in transforming chronic disease management. Furthermore, the insights gained from the literature review and our benchmarking analysis may offer valuable guidance for healthcare providers, patients, and policymakers as they navigate the evolving landscape of digital health.

Looking ahead, the future of SaMD and mHealth applications appears promising, with the potential to significantly enhance the quality of care for patients with chronic diseases.

Sofoklis Kyriazakos, CEO

The post SaMD & mHealth apps: Status and promising results for improvement of health outcomes appeared first on Healthentia.

Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data

System — Thu, 18 Nov 2021 13:42:03 +0000

Topics: Decision support systems and personalized interventions for workability sustainability; Unobtrusive and pervasive health monitoring at the workplace

SOURCE: SciTePress, 2021; BOOK DOI Link, Chapter DOI Link

Breaking up Long Sedentary Periods of Office Workers through a Virtual Coach using Activity Data

Jasmijn Franke ^1,2 ; Christiane Grünloh ^1,2; Dennis Hofs ² ; Boris Van Schooten ² ; Andreea Bondrea ² ; Miriam Cabrita ^1,2,3

¹Biomedical Signals and Systems Group, University of Twente, Enschede, The Netherlands

²eHealth Group, Roessingh Research and Development, Enschede, The Netherlands

³Innovation Sprint Sprl, Brussels, Belgium

Abstract

Office workers often lead sedentary lifestyles, a lifestyle responsible for higher risks of cardiovascular disease, stroke, diabetes and premature mortality. Improvements towards a more active lifestyle reduce cardiovascular risks and thus changing the sedentary lifestyle might prevent chronic illness. The Recurring Sedentary Period Detection (RSPD) algorithm described in this paper was designed to identify recurring sedentary periods using data from an activity tracker, summarise the sedentary periods and pinpoint notification times at which the user should be motivated to get some movement. The outcome of the RSPD algorithm was validated using data from a 10-week period of one typical office worker. Our results show that the RSPD algorithm could correctly identify the recurring sedentary periods, compute fitting daily summaries and pinpoint the notification times correctly. With minor differences, the RSPD algorithm was successfully implemented in the healthyMe smartphone application, one of the supporting services of the SMARTWORK project. Within the healthyMe application, an embodied virtual agent is used to communicate the daily summaries and motivate the user to move more at the identified notification times. Pilots planned as part of the SMARTWORK project will evaluate whether the RSPD algorithm helps to motivate office workers to break up sedentary periods.

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The Results of an Iterative Evaluation Process of an Mhealth Application for Rewarding Healthy Behaviour Among Older Adults

System — Sat, 02 Oct 2021 14:26:07 +0000

Topics: eHealth, Mhealth, e-Health applications

SOURCE: Springer Link 2020, Aug.; BOOK DOI Link, Chapter DOI Link

The Results of an Iterative Evaluation Process of an Mhealth Application for Rewarding Healthy Behaviour Among Older Adults

Stephanie Jansen-Kosterink ; Roos Bulthuis ; Silke ter Stal ; Lex van Velsen ; Aristodemos Pnevmatikakis ; Sofoklis Kyriazakos ; Andrew Pomazanskyi ; Harm op den Akker

Abstract

It is a challenge to find effective ways for supporting older adults to increase their levels of physical activity and develop habitual physical activity behaviours. Within the GOAL project, an mHealth intervention to motivate older adults to be active was developed, by blending the iterative design and the evaluation activities. The aim of this paper is to present the results of the iterative evaluation process of this mHealth intervention. Evaluation end-points were usability, user experience and potential effect. In total, four cycles of evaluation and redesign of GOAL were conducted in order to create value-adding technology, and demonstrate its impact. Each cycle contained test-weeks, weeks for data analysis, and time for technical modification. In total, 28 participants (students and older adults) interacted with GOAL for a total of 476 days and provided their feedback. During the process, various usability issues were solved to improve GOAL. The users rated the usability of GOAL as acceptable. Older adults were positive about the idea to encourage a healthy lifestyle by using GOAL. During the final evaluation cycle, GOAL encouraged older adults to be more active and motivated them to reach their daily goal.

Keywords: Older adults; eHealth; mHealth; Games; Rewards; Iterative design; Usability testing; Evaluation

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