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BackgroundSoftware as a Medical Device (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 evidence-based personalized treatment plans, risk prediction, and enhanced clinical outcomes.ObjectiveThe systematic literature review aims to provide a comprehensive overview of the status of SaMD and mHealth apps, highlight the promising results, and discuss what is the potential of these technologies for improving health outcomes.MethodsThe research methodology was structured in two phases. In the first phase, a search was conducted in the EuropePMC (EPMC) database up to April 2024 for systematic reviews on studies using the PICO model. The study population comprised individuals afflicted by chronic diseases; the intervention involved the utilization of mHealth solutions in comparison to any alternative intervention; the desired outcome focused on the efficient monitoring of patients. Systematic reviews fulfilling these criteria were incorporated within the framework of this study. The second phase of the investigation involved identifying and assessing clinical studies referenced in the systematic reviews, followed by the synthesis of their risk profiles and clinical benefits.ResultsThe results are rather positive, demonstrating how SaMDs can support the management of chronic diseases, satisfying patient safety and performance requirements. The principal findings, after the analysis of the extraction table referring to the 35 primary studies included, are: 24 studies (68.6%) analyzed clinical indications for type 2 diabetes mellitus (T2DM), six studies (17.1%) analyzed clinical indications for cardiovascular conditions, three studies (8.7%) analyzed clinical indications for cancer, one study (2.8%) analyzed clinical indications for chronic obstructive pulmonary disease (COPD), and one study (2.8%) analyzed clinical indications for hypertension. No severe adverse events related to the use of mHealth were reported in any of them. However, five studies (14.3%) reported mild adverse events (related to hypoglycemia, uncontrolled hypertension), and four studies (11.4%) reported technical issues with the devices (related to missing patient adherence requirements, Bluetooth unsuccessful pairing, and poor network connections). For what concerns variables of interest, out of the 35 studies, 14 reported positive results on the reduction of glycated hemoglobin (HbA1c) with the use of mHealth devices. Eight studies examined health-related quality of life (HRQoL); in three cases, there were no statistically significant differences, while the groups using mHealth devices in the other five studies experienced better HRQoL. Seven studies focused on physical activity and performance, all reflecting increased attention to physical activity levels. Six studies addressed depression and anxiety, with mostly self-reported benefits observed. Four studies each reported improvements in body fat and adherence to medications in the mHealth solutions arm. Three studies examined blood pressure (BP), reporting reduction in BP, and three studies addressed BMI, with one finding no statistically significant change and two instead BMI reduction. Two studies reported significant weight/waist reduction and reduced hospital readmissions. Finally, individual studies noted improvements in sleep quality/time, self-care/management, six-minute walk distance (6MWD), and exacerbation outcomes.ConclusionThe systematic literature review demonstrates the significant potential of software as a medical device (SaMD) and mobile health (mHealth) applications in revolutionizing chronic disease management through remote patient monitoring (RPM) and digital therapeutics (DTx). The evidence synthesized from multiple systematic reviews and clinical studies indicates that these technologies, exemplified by solutions like Healthentia, can effectively support patient monitoring and improve health outcomes while meeting crucial safety and performance requirements. The positive results observed across various chronic conditions underscore the transformative role of digital health interventions in modern healthcare delivery. However, further research is needed to address long-term efficacy, cost-effectiveness, and integration into existing healthcare systems. As the field rapidly evolves, continued evaluation and refinement of these technologies will be essential to fully realize their potential in enhancing patient care and health management strategies.

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

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).

Discovery of biomarkers is a continuous activity of the research community in the clinical domain that recently shifted its focus toward digital, non-traditional biomarkers that often use physiological, psychological, social, and environmental data to derive an intermediate biomarker. Such biomarkers, by triggering smart services, can be used in a clinical trial framework and eHealth or digital therapeutic services. In this work, we discuss the APACHE trial for determining the quality of life (QoL) of cervical cancer patients and demonstrate how we are discovering a biomarker for this therapeutic area that predicts significant QoL variations. To this extent, we present how real-world data can unfold a big potential for detecting the cervical cancer QoL biomarker and how it can be used for novel treatments. The presented methodology, derived in APACHE, is introduced by Healthentia eClinical solution, and it is beginning to be used in several clinical studies.

The health emergency linked to the SARS-CoV-2 pandemic has highlighted problems in the health management of chronic patients due to their risk of infection, suggesting the need of new methods to monitor patients. People living with HIV/AIDS (PLWHA) represent a paradigm of chronic patients where an e-health-based remote monitoring could have a significant impact in maintaining an adequate standard of care. The key objective of the study is to provide both an efficient operating model to “follow” the patient, capture the evolution of their disease, and establish proximity and relief through a remote collaborative model. These dimensions are collected through a dedicated mobile application that triggers questionnaires on the basis of decision-making algorithms, tagging patients and sending alerts to staff in order to tailor interventions. All outcomes and alerts are monitored and processed through an innovative e-Clinical platform. The processing of the collected data aims into learning and evaluating predictive models for the possible upcoming alerts on the basis of past data, using machine learning algorithms. The models will be clinically validated as the study collects more data, and, if successful, the resulting multidimensional vector of past attributes will act as a digital composite biomarker capable of predicting HIV-related alerts. Design: All PLWH > 18 sears old and stable disease followed at the outpatient services of a university hospital (n = 1500) will be enrolled in the interventional study. The study is ongoing, and patients are currently being recruited. Preliminary results are yielding monthly data to facilitate learning of predictive models for the alerts of interest. Such models are learnt for one or two months of history of the questionnaire data. In this manuscript, the protocol—including the rationale, detailed technical aspects underlying the study, and some preliminary results—are described. Conclusions: The management of HIV-infected patients in the pandemic era represents a challenge for future patient management beyond the pandemic period. The application of artificial intelligence and machine learning systems as described in this study could enable remote patient management that takes into account the real needs of the patient and the monitoring of the most relevant aspects of PLWH management today.

The way one leads their life is considered an important factor in health. In this paper we propose a system to provide risk assessment based on behavior for the health insurance sector. To do so we built a platform to collect real-world data that enumerate different aspects of behavior, and a simulator to augment actual data with synthetic. Using the data, we built classifiers to predict variations in important quantities for the lifestyle of a person. We offer a risk assessment service to the health insurance professionals by manipulating the classifier predictions in the long-term. We also address virtual coaching by using explainable Artificial Intelligence (AI) techniques on the classifier itself to gain insights on the advice to be offered to insurance customers.