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

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A digital therapy pathway is a healthcare method that employs technology to provide support and therapy services to patients remotely.  These technologies can include wearables and a mobile app for collecting data, an online platform for visualization purposes, video conferencing and chatting for direct communication between clinicians and patients, and a virtual companion service that can support the patient by giving personalized advice [2].

Typically, the process of digital therapy includes many steps [3]. These can include:

1. A first assessment of each patient to determine the best therapy approach
2. Set of clear goals that are specific, measurable, and achievable defined by healthcare professionals
3. Progress monitoring with the use of questionnaires and other digital tools allows healthcare professionals to keep track of the effectiveness of the treatment and adjust it as required

For the last step, a digital evaluation method of the data collected can be implemented where the predefined goals are transformed into critical rules for the health progression. This performance assessment can automatically alert the patient through the mobile app when a measurable attribute is out of range or provide guidance, feedback, and support as patients work towards achieving their goals. This will be achieved with the use of a virtual companion service, that will trigger a dialogue on a regular basis in order to give advice in a personalized manner to ensure progress towards the goals.

To conclude, digital therapy pathways can be an effective way to deliver therapy services to patients who need continuous monitoring and have difficulty accessing traditional in-person therapy, such as those who have mobility issues and also it can thrive in areas where demand for traditional treatment outgrows its capacity [3].

Up to this point, digital therapy is mostly used to supply psychotherapy [4] and Healthentia can be used to provide such services as it incorporates all the aforementioned tools, aspiring to effectively support also other diseases than mental health conditions. Healthentia can provide feedback and in combination with a virtual coaching service, orchestrated by a predefined digital clinical pathway, it can create a personalized approach to improve someone’s health. However, it is important to always take into account patients’ mental health needs, as in some cases in-person therapy may be required.

References

[1] “Global Digital Therapeutics Market Report 2022: Analysis & Forecasts 2020-2026 – Market to Reach $12.1 Billion by 2026” (accessed May 15, 2023).

[2] C. Moore, “What Is Digital Therapy and How Does It Really Work?”, Quenza, Aug. 13, 2021. (accessed May 15, 2023).

[3] “Digital Therapeutics: How Software Can Treat Diseases” AltexSoft. (accessed May 15, 2023).

[4] A. Nwosu, S. Boardman, M. M. Husain, and P. M. Doraiswamy, “Digital therapeutics for mental health: Is attrition the Achilles heel?”, Frontiers in Psychiatry, vol. 13, 2022, Accessed: May 15, 2023. [Online].

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Healthentia is a digital platform by Innovation Sprint that facilitates data capture in hybrid clinical trials and enables digital therapeutics as a certified medical device. It does so by offering a smartphone application for patients and a web portal for investigators and healthcare professionals to securely access smart services and insights. Healthentia empowers patients in clinical studies and gives them an active role in taking trial actions, rather than bringing them to the trial sites. Healthentia is used by Top5 Pharma and hospitals and operates under the strict regulatory framework of Good Clinical Practice.

In 2021, Healthentia medical device entered the market of Patient Support Programs (PSP) as a decision support software intended to monitor, detect and predict outcomes, offer virtual coaching services and generate automatic alerts regarding events, based on Real World Data gathered from patients. Healthentia PSPs are offered jointly with healthcare organizations and pharmaceutical companies and follow co-development, participatory-design, and patient-centric approaches. In 2022 Healthentia is expected to collect evidence that will create clinical claims on its efficacy as a therapeutic device.

What is a Patient Support Program (PSP)?

A PSP provides an integrated approach to remote care with digital services to support patients during medical treatments. Today, digital platforms for remote patient monitoring are the technical infrastructure of PSPs, offering notifications services for therapy, functionalities for booking hospital visits, and questionnaires to be filled in. More advanced solutions enable the capturing of data from wearable and other medical devices, as well as teleconsultation and virtual coaching as a therapy.

How can a digital platform transform healthcare?

If we can better educate patients and enable them to share data between the periodic visits, we can improve their engagement in daily self-management of their health and the interactions with the care team by personalizing the care plan, making sure they understand why adherence matters, and providing ways to individually achieve it, patients will increase their understanding of care plan, medication regimen and their engagement with care plan activities and confidence in disease management. By using the PSP app and staying connected with the care team, patients will be able to stay engaged in remotely reporting their clinical metrics, wellbeing, and adherence in-between visits. Finally, by having new remote monitoring data, care plans can be adjusted at the follow-up visits, and situations that require clinical attention can be identified earlier.

Do you want to know more? Please visit www.healthentia.com

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CATEGORY: eHealth, patient reported outcomes, e-clinical platform, smart eHealth

SOURCE: Frontiers Comp. Sci., 16 December 2021;  BOOK DOI Link, Chapter DOI Link

Digital Therapeutics: Virtual Coaching Powered by Artificial Intelligence on Real-World Data

Harm op den Akker ; Miriam Cabrita ; Aristodemos Pnevmatikakis ^*

Innovation Sprint, Brussels, Belgium 
Abstract

An ever-increasing number of people need to cope with one or more chronic conditions for a significant portion of their life. Digital Therapeutics (DTx) focused on the prevention, management, or treatment of chronic diseases are promising in alleviating the personal socio-economic burden caused. In this paper we describe a proposed DTx methodology covering three main components: observation (which data is collected), understanding (how to acquire knowledge based on the data collected), and coaching (how to communicate the acquired knowledge to the user). We focus on an emerging form of automated virtual coaching, delivered through conversational agents allowing interaction with end-users using natural language. Our methodology will be applied in the new generation of the Healthentia platform, an eClinical solution that captures clinical outcomes from mobile, medical and Internet of Things (IoT) devices, using a patient-centric mobile application and offers Artificial Intelligence (AI) driven smart services. While we are unable to provide data to prove its effectiveness, we illustrate the potential of the proposed architecture to deliver DTx by describing how the methodology can be applied to a use-case consisting of a clinical trial for treatment of a chronic condition, combining testing of a new medication and a lifestyle intervention, which will be partly implemented and evaluated in the context of the European research project RE-SAMPLE (REal-time data monitoring for Shared, Adaptive, Multi-domain, and Personalised prediction, and decision making for Long-term Pulmonary care Ecosystems).

Keywords: digital biomarkers, machine learning, ai clinical trials, Healthentia, real-world data, e-clinical platform

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A biomarker is a naturally occurring characteristic by which a pathological or physiological process can be identified. A digital biomarker comprises of objective, quantifiable physiological and behavioral data, measured utilising digital portable, wearable, implantable or digestible devices, to be used to predict and manage health-related outcomes.

Innovation Sprint has built a composite contextual biomarker-based οn multiple aspects of Real-World Data (RWD), collected from people unobtrusively, while following-up their normal living routine. It is composite in the sense that it is not based on a single measurement, but rather on multiple diverse measurements (objective RWD) and peoples’ reports (subjective RWD). It is contextual in the sense that not only the person is measured, but also the person’s lifestyle context: social and environmental aspects complement the more traditional physiological and psychological ones.

Our RWD

At Innovation Sprint we are strong advocates of the empirical knowledge that lifestyle is a strong determinant of health. Hence our biomarker is based on RWD spanning four important aspects of a person’s lifestyle:

 Physiological RWD quantifies physical behaviour (active vs sedentary lifestyle as measured by steps walked, floors climbed, activity types, minutes in different intensity levels or heart rate zones, resting heart rate, sleep characteristics) and includes body info (height, weight, gender, race), nutrition (water, other liquids, food) and symptoms (body temperature, cough, diarrhea, headache, nausea, pain, etc.).

 Psychological RWD quantifies at a simple level mood, and in more complex situations mental state collected via elaborate, domain-specific questionnaires. Measurements can also play a role, either directly e.g. facial expression recognition, or indirectly, e.g. weather where people are living).

 Social RWD quantifies social activity of people. This can be measured indirectly from the usage of the phone (diversity, duration, frequency of calls) and social media (diversity, number, frequency of interactions). More direct information can be reported using questionnaires on activities with friends, family or co-workers.

 Environmental RWD indicates the quality of life. Usually, reported by the users. Measurements of living or working environment quality are made with commercial devices (e.g. air quality meters).

AI for discovering our biomarker

Biomarker discovery at Innovation Sprint is done in three stages

Definition stage, where the domain experts select the clinically significant outcomes that need to be predicted by the biomarker(s).

Manual RWD selection stage, where domain knowledge is applied to refine our generic RWD selection into those lifestyle aspects that are relevant to the disease/condition in question.

Iterative design stage: Machine Learning/AI algorithms are used to train a proprietary classifier using the elected RWD to predict the selected clinically significant outcomes. The classifier is applied on new data  yielding predictions and insights leading to digital therapeutics.

Validating our approach

We employed RWD collected over 7 years to train a biomarker that predicts significant weight changes. Such a biomarker is important for patients with several diseases (e.g. NAFLD), as well as for the general population interested in well-being. We achieved over 80% or correct prediction of the outcome, while we also analysed the different RWD aspects that led each individual to positive or negative outcomes, in order to offer personalized coaching services.

As we speak, we are utilising the same approach in other therapeutic areas, e.g. cervical cancer, to predict low toxicity events. Starting from 2021 we will validate this hypothesis in much larger cohorts, targeting –among others- COPD patients with Cardiovascular Disease comorbidities.

We will keep you update on our observations and findings!

Aristodemos Pnevmatikakis
R&D Director, Innovation Sprint

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