Risky lifestyle behaviors remain one of the most pressing challenges in global health, contributing to the rising burden of chronic diseases. Despite wide recognition of the need for healthier long-term habits, achieving meaningful and lasting behavioral change continues to be difficult. Traditional interventions often fall short in promoting sustained motivation, and cost-effective digital solutions that adapt to individual needs are still limited.

At Healthentia, we are committed to bridging this gap by leveraging science-driven innovation in digital health. Our latest research presents a holistic framework designed to transform lifestyle behavior change through personalized digital coaching, powered by an automated multiagent reasoning system.

A Science-Backed Multiagent Framework

This framework brings together established behavioral change science and the practical strength of Healthentia’s certified SaMD (Software as a medical device). At its core, the system uses multiagent reasoning to select the most effective coaching strategies for everyone based on personal assessments and daily health and lifestyle data.

By prioritizing evidence-based BCTs (Behavioral Change Techniques) with the strongest impact on change, Healthentia’s digital coaching adapts in real time to user progress and engagement patterns. This ensures that interventions are not only relevant but also sustainable, empowering patients to take ownership of their health journey.

Application in Type 2 Diabetes Management

To illustrate this approach, we applied the behavioral change coaching scheme in a study involving patients with Type 2 Diabetes during a total of 12 weeks intervention plan with a 2-week assessment and adherence control weeks. Nine participants were monitored using Healthentia SaMD for remote care and personalized coaching content delivery.

The outcomes were highly encouraging. Within the group, fasting glucose levels dropped by an average of –17.3 mg/dL, supported by a large effect size and strong statistical significance. Patients also experienced meaningful reductions in weight (–2.89 kg) and BMI (–1.05 kg/m²). Importantly, the personalized coaching delivered through Healthentia resonated with patients, with over 71% of the opened, recommended content received positively.

These results demonstrate the potential of combining behavioral science with adaptive digital coaching to create measurable improvements in health outcomes.

What This Means for the Future

While these findings are preliminary, they highlight a promising path forward. Future directions include refining the multiagent selection process, expanding the type 2 diabetes program with more comprehensive health markers, and broadening the applications of this framework to other chronic conditions.

Further to the CE-marked virtual coaching module of Healthentia, wellbeing coaching support that is supported by genAI technologies can have strong influence to the patients’ behavior. This is to be studied and presented in future work.

Healthentia’s mission is to advance decentralized care with digital tools that are not only clinically robust but also deeply human-centered. By fusing data-driven intelligence with personalized coaching, we are helping patients break free from the limitations of one-size-fits-all health advice—giving them the right support at the right time.

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CATEGORY: Digital Health

SOURCE: JMIR Publications Advancing Digital Health & Open Science, July 2025, doi:10.2196/73807

Multidomain Behavioral Change Digital Coaching for Chronic Disease Management in Patients With Type 2 Diabetes: Framework Development and Preliminary Evaluation

 Konstantina Kostopoulou¹, Danae Lekka¹, Aristodemos Pnevmatikakis¹, Nellie Angelova¹, Stefanos Tamouridis³, Panos Stafylas^1,2, Alexandra Bargiota³, Sofoklis Kyriazakos¹

¹Innovation Sprint Srl, Belgium

²Healthlink

³University of Thessaly, Greece

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The paper highlighted how the platform addressed the fragmented nature of heart failure care by enabling continuous communication between hospital specialists and community healthcare providers.

Study Design and Implementation

The AZIMUTH study (FondAZione A. Gemelli IRCCS Artificial Intelligence Empowered Digital PlatforM to sUpport paTients with Heart Failure) addressed this healthcare challenge through innovative digital health technology. It demonstrates how smartphone-based care could transform outcomes for heart failure patients. This multicenter, prospective study enrolled 300 heart failure patients across four leading Italian medical centers in Phases 1 and 2.

The study utilized Healthentia v3, a Class I Software as Medical Device (SaMD), as the core platform for remote patient monitoring and care delivery operating through two integrated components:

Patient Mobile Application: Patients used intuitive “widgets” for daily health monitoring, including mandatory weight and blood pressure tracking, validated questionnaires (Kansas Questionnaire and medication adherence assessments), and optional monitoring of heart rate, oxygen saturation, and physical activity. The app seamlessly integrated with Bluetooth-enabled devices to minimize manual entry errors.

Clinical Dashboard: Healthcare providers accessed a platform that consolidated patient data into actionable insights, featuring real-time monitoring, intelligent alert systems based on clinical thresholds, longitudinal trend analysis, and integrated patient communication tools.

The study carefully addressed the real-world implementation by establishing clear inclusion criteria that balanced technological requirements with practical applicability. Patients required basic digital literacy or caregiver support, smartphone compatibility, and received comprehensive training during enrollment. The platform was designed for intuitive use across age groups, ensuring broad accessibility.

Proven Results

The study successfully demonstrated the effectiveness of digital health solutions in heart failure management:

Patient Engagement: Achieved the primary objective with over 70% of patients successfully engaging with the digital platform throughout the study period.

Clinical Improvements: Patients showed significantly improved medication adherence and treatment engagement compared to traditional care approaches.

Healthcare Provider Value: Clinicians reported enhanced ability to monitor patients remotely, identify early warning signs, and coordinate care more effectively.

Feasibility Confirmed: The app-based model proved both technically feasible and clinically valuable across diverse patient demographics.

Impact and Validation

AZIMUTH validated that smartphone-based care could transform outcomes for heart failure patients – a population traditionally challenged by frequent hospitalizations and complex medication regimens. The study proved that digital health solutions, when properly implemented through certified medical device platforms like Healthentia, could enhance both patient outcomes and healthcare efficiency.

The successful completion of AZIMUTH Phase 1 established a new evidence base for digital health adoption in chronic disease management. Azimuth demonstrated that collaborative innovation between healthcare institutions, pharmaceutical companies, and technology providers can deliver measurable clinical value. A second paper with results from Phase II is expected soon and new partners have joined efforts in a new study named Azimusa utilizing the Azimuth care model and expanding further the scope to more medical centers in the north with more patients.

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What Is B-COMPASS?

At its core, B-COMPASS helps all stakeholders in Healthcare from clinicians, caregivers, software providers, and healthcare policymakers to identify the drivers behind patient behavior. It relies on a concise set of fewer than ten questions to generate a patient profile based on five key dimensions: health consciousness, treatment needs, personal concerns, acceptance, and perceived control.

This simplicity makes it powerful. It enables the creation of tailored communication and support strategies to address the unique challenges each patient faces, ultimately aiming to improve adherence and treatment outcomes.

How Innovation Sprint Is Contributing

As a proud partner in the BEAMER project, Innovation Sprint brings deep experience in real-world data collection, behavioral modeling, and chronic disease management through our flagship Software as a Medical Device (SaMD), Healthentia.

With years of insights from managing patient journeys, particularly around non-adherence patterns, Innovation Sprint contributes to shaping and validating the B-COMPASS model with real-world scenarios. Our involvement ensures the model reflects actual patient needs and clinical realities, making it both usable and impactful in digital health platforms.

Through Healthentia, we have seen firsthand how variable patient engagement can be, and how crucial it is to adapt digital interventions to individual needs. Integrating tools like B-COMPASS enhances our ability to design adaptive programs that not only monitor but also positively influence patient behavior over time.

Why B-COMPASS Matters for SaMDs like Healthentia

Tools like B-COMPASS don’t just provide insight, they enable action. For SaMDs, this means gaining the ability to segment patients more meaningfully and respond with personalized interventions, educational content, and coaching.

The result? Increased treatment adherence, improved patient satisfaction, and reduced healthcare burden—goals at the heart of both BEAMER and Innovation Sprint’s mission.

Looking Forward

As B-COMPASS matures within the BEAMER framework, its integration into digital health ecosystems like Healthentia will further personalize chronic disease management. We believe this marks a pivotal step toward scalable, human-centered digital healthcare.

To learn more about B-COMPASS and the BEAMER project, visit https://beamerproject.eu/BEAMER-model/.

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CATEGORY: Digital Health

SOURCE: European Heart Journal – Digital Health, April 2025, https://doi.org/10.1093/ehjdh/ztaf040

Study design and rationale of the AZIMUTH trial: a smartphone, app-based, E-health-integrated model of care for heart failure patients

Domenico D’Amario¹, Attilio Restivo^2,3, Renzo Laborante^2,3, Donato Antonio Paglianiti^2,3, Alfredo Cesario^2,3,4,5, Stefano Patarnello², Sofoklis Kyriazakos⁶, Alice Luraschi², Konstantina Kostopoulou⁶, Antonio Iaconelli^2,3, Enrico Incaminato¹, Gaetano Rizzo¹, Marco Gorini⁷, Stefania Marcoli⁷, Vincenzo Bartoli⁷, Thomas Griffiths⁸, Peter Fenici^3,7,9, Simona Giubilato¹⁰, Maurizio Volterrani¹¹, Giuseppe Patti¹, Vincenzo Valentini^2,3, Giovanni Scambia^2,3, Filippo Crea^3,12

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CATEGORY: Digital Public Health

SOURCE: Frontiers – Public Health, December 2024, Volume 12-2024; https://doi.org/10.3389/fpubh.2024.1488687

Benchmarking the clinical outcomes of Healthentia SaMD in chronic disease management: a systematic literature review comparison

Sofoklis Kyriazakos¹ , ^*Aristodemos Pnevmatikakis¹, Konstantina Kostopoulou¹, Laurent Ferrière² , Kyun Thibaut² , Erika Giacobini³ , Roberta Pastorino^3,4 , Marco Gorini⁵ , Peter Fenici⁵

¹Innovation Sprint srl, Clos Chapelle-aux-Champs, Brussels, Belgium

²COVARTIM, Watermael-Boitsfort, Belgium

³Section of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy

⁴Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

⁵AstraZeneca SpA, Milano Innovation District (MIND), Milano, Italy

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Managing chronic obstructive pulmonary disease (COPD) alongside other chronic conditions (CCs) like depression requires a comprehensive and innovative approach. As part of the RE-SAMPLE research project, Healthentia, our advanced SaMD solution, addresses this challenge by integrating real-world data (RWD) to enhance disease management and improve daily life for patients. For more information about the intended use of the device and the medical modules, please consult: https://healthentia.com/medical-device/

This research-driven initiative bridges the gap between clinical research and real-world healthcare, supporting two phases of COPD and CCs management:

• Phase 1: Healthentia collects RWD to track disease progression, uncover patterns, and predict exacerbations. These insights enable proactive care, empowering healthcare providers to anticipate and address patient needs more effectively.
• Phase 2: Through its Virtual Companionship Program (VCP), the platform delivers tailored self-management tools, including lifestyle coaching, goal-setting, and real-time medical suggestions. Patients gain the confidence to manage their health actively, while healthcare providers benefit from actionable insights via a clinical dashboard. This enables personalized, adaptive care strategies, ensuring better outcomes.

Healthentia redefines the standard of COPD care in hospitals across Europe by seamlessly integrating real-world insights with technology-driven solutions. By prioritizing both patients and providers, it transforms the approach to managing not only COPD but also co-existing chronic conditions, helping patients regain control over their health and well-being.

This World COPD Day, Healthentia reaffirms its commitment to innovation, providing hope and support for millions living with COPD.

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World Diabetes Day is an essential reminder of the global impact of diabetes. Especially type 2 diabetes affects millions of lives and continues to grow at an alarming rate. Currently, type 2 diabetes makes up more than 90% of the over 450 million adults worldwide that are living with diabetes. By 2030, this number is expected to reach 578 million. This increase not only strains healthcare systems but places a significant burden on individuals who must learn to live with and manage this chronic condition.

At Innovation Sprint, we believe in empowering patients by providing Healthentia, the patient companion app they need to effectively manage their condition at home. Healthentia is designed for chronic disease management, offering a personalized approach to each patient. By collecting data on daily habits, physical activity, and health metrics, Healthentia offers customized support and advice that aligns with the patient’s health goals and current state. For more information about the intended use of the device and the medical modules, please consult: https://healthentia.com/medical-device/.

For type 2 diabetes patients, effective management is key to preventing serious complications like heart disease and nerve damage. While healthcare professionals are essential, most daily care happens at home, requiring regular monitoring, a balanced diet, physical activity, and adherence to treatments. Many patients find this challenging, especially without personalized guidance.

In our ongoing research study with type 2 diabetes patients, we are taking this a step further. By gathering detailed data, we can provide personalized recommendations that evolve alongside the patient. For instance, if a patient’s activity level drops or they struggle with diet management, Healthentia can recognize these patterns and suggest targeted interventions that encourage gradual improvements. This constant, personalized feedback loop helps patients feel supported and more capable of taking control of their health.

On this World Diabetes Day, let’s acknowledge not only the challenges but also the strides being made in digital health to enhance self-management for millions of people worldwide. With solutions like Healthentia, we’re creating a future where living with diabetes doesn’t have to be so daunting.

“In our new study with Diabetes Mellitus Type 2 patients in the University of Thessaly we are integrating Healthentia, a digital health solution, to evaluate how digital health tools can enhance traditional healthcare. The patients thus far responded positively, embracing the mobile app for their support at home, while the healthcare professionals benefit from the web portal, which provides real-time data monitoring and insight into the tailored advice each patient receives.” 

Tamouridis Stefanos MD, MSc
Clinic of Endocrinology and Metabolic Diseases, General University Hospital of Larissa
University of Thessaly

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Our experiments indicate that model quality strongly depends on the data, both its volume and quality. Data volume is determined by the number of patients having already used Healthentia for a particular pathology. Data quality is determined by each patient’s adherence to the data collection plan. Both data volume and quality are seldom ideal. At least during the first studies Healthentia is used for a particular pathology, there are simply not enough patients to give us a satisfying volume of data. Moreover, patients are of varying degrees of capability to collaborate with Healthentia’s mobile app. Omissions and errors can happen, and then reduce data quality.

Training with suboptimal data volume and quality leads to suboptimal models, suffering from overfitting and bias. The alternative is to reside to the knowledge of the experts, the healthcare professionals. Their knowledge is captured within rule-based expert systems. That yield the necessary analysis of the patients. At Innovation Sprint we have been experimenting with ways to combine the information the data offers and the expertise the healthcare professionals offer. The HumAIne project offers Healthentia the novel tools to achieve this combination.

HumAIne is an EU co-funded project under the topic HORIZON-CL4-2022-HUMAN-02-01 that started in October 2023. HumAIne facilitates advanced and reliable collaboration of experts and AI towards hybrid decision making and support in a variety of industries. It delivers the HumAIne Operating System, built on four technological pillars: Active Learning, Neuro-Symbolic Learning, Swarm Learning, and eXplainable AI. The HumAine OS enables AI solution creators to build advanced Human-AI collaboration systems that outperform standalone AI and individual experts’ efforts.

Innovation Sprint leads the project’s efforts on defining the vision and specifications for human-AI collaboration, focusing on user requirements extraction and use case scenarios definition. These efforts lead on the needs to be covered by the technology developed in HumAIne. We also lead the healthcare pilot, where the HumAIne technology will be leveraged to enhance the AI modules in the patient understanding and advice delivery systems of Healthentia. More specifically, we will be employing Neuro-Symbolic Learning to learn models not simply on data, but on the combined information of data and healthcare professionals’ expertise. We will be comparing the models learnt traditionally to those employing Neuro-Symbolic Learning, both in terms of prediction metrics but also in terms of their effectiveness in the overall behavioral change framework of Healthentia.

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Definition & importance

• Clinical data from electronic health records (EHRs) and case report forms (EHRs). This data establishes who the subject is, providing demographics, family history, comorbidities, procedure and treatment history, and outcomes. Such data types are also common in clinical trials.
• Patient-generated data from patient-reported outcome (PRO) questionnaires, or measurements from wearables. This is data collected in everyday setting, providing insights directly from the patient, beyond clinic visits, procedures, and hospitalization. While patient-generated data is not unusual in clinical trials, it is collected in a centralized manner at the regular visits of the trial volunteers to the healthcare facilities. In the real-world context, the collection is done continuously at home.
• Public and government data including cost and utilization data. Such data provides information on the healthcare system and the different stakeholders therein.

RWD: Collecting in a clinical vs. everyday setting

Patient-generated, everyday RWD types

• Patient-reported via questionnaires: This collection model is closer to the established clinical trial approach, but this time the questionnaires are digital, pushed to subjects via some companion mobile app. They mostly have to do with self-assessment of different aspects.
• Patient-reported via widgets: Similar to questionnaires, only this time rich graphical interfaces are employed. The widgets allow manual entry, or take advantage of integration with 3^rd party devices meant for occasional use like scales or blood pressure monitors to automatically collect measurements.
• Automatically reported by wearables: Continuous measurements from wearable devices is one of the most prominent sources of RWD. Ubiquitous activity trackers or more specialized devices like sleep monitors are integrated either at device level (when a Software Development Kit is available, e.g., via Apple Health Kit) or at device cloud level (when an Application Programming Interface is available).

• Physiological: Data about physical activity, continuous monitoring of vitals, sleep
• Psychological: Emotions
• Social: Interactions (phone calls, social media)
• Environmental: Living and working environment

Learning on RWD

RWD in Healthentia

• The Learning Services for training models
• The Inference Services for inferring with the help of the trained models
• The Clinical Pathway for utilizing the raw RWD and the inference results in monitoring the state of subjects, and
• The Virtual Coach for utilizing all the above in personalized advice given to the subjects.

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