The demographic pressure behind the work is stark. According to United Nations projections cited in the study, the global population aged 65 and over is expected to overtake the number of children by the late 2070s, reaching 2.2 billion people. In Europe, the share of working-age individuals is shrinking while the elderly proportion climbs, and a recent meta-analysis found that roughly 46 percent of older adults worldwide live with two or more chronic conditions such as cardiovascular disease, diabetes or movement disability. Loneliness, cognitive impairment and depression compound the physical burden, making continuous, low-friction monitoring at home an increasingly attractive alternative to frequent clinical visits.

The research team, led by Azad Aghajani and Marco Ciro Liscio with colleagues at Marche Polytechnic University and EUAbout in Brussels, selected five of the most common home-monitoring devices and mapped each one across SGAM’s three dimensions: five interoperability layers running from business objectives down to physical components, domains covering the energy conversion chain, and zones representing hierarchical levels of system management from the process level up to the enterprise. In their healthcare adaptation, the elderly person’s home and its home area network occupy the customer-premises domain, while device measurements sit at the process and field levels, gateway aggregation at the station level, and monitoring and policy functions at the operation and enterprise levels. The market zone of the original model was deliberately excluded because the framework targets home care rather than electricity-market interactions.

Each layer carries a distinct technical role. The business layer defines the goals of continuous health monitoring, cost reduction and energy saving, alongside regulatory compliance with EU law, the General Data Protection Regulation, CE marking and medical device standards. The function layer covers the actual capabilities: vital-sign collection, power management, battery optimization and smart-grid integration that lets a device adjust its consumption in response to real-time energy prices. The information layer handles secure storage, encryption and interoperability with hospital information systems and electronic health records, while the communication layer governs local Bluetooth and Wi-Fi links between devices and the home gateway, wide-area connections to cloud platforms, and standards such as IEEE 11073 for personal health devices and IEC 61850 for energy-related data exchange. The component layer grounds everything in the physical hardware: sensors, microcontrollers, batteries, firmware and the IoT gateways that aggregate data.

What makes the framework conceptually interesting is that it treats healthcare devices as citizens of the smart home rather than isolated gadgets. A blood pressure monitor that knows when electricity is expensive can defer non-urgent synchronization tasks; a gateway that aggregates ECG data locally using edge computing can reduce latency and bandwidth while forwarding only clinically relevant information. The authors argue that the real value of SGAM in this context is not energy saving at the device level but the provision of a common architectural language in which heterogeneous devices, communication networks, cloud services and healthcare providers can be coordinated, with interoperability requirements identified systematically across layers rather than patched together ad hoc.

To test whether the vision makes financial sense, the team ran a cost-benefit analysis over a five-year horizon using secondary European healthcare data, manufacturer specifications and market-based estimates. The total five-year cost of owning all five devices came to 600 euros: 240 euros in purchase prices plus 360 euros in batteries and maintenance. Against this, the reference scenario assumed an annual benefit of 330 euros, derived conservatively from the lower bounds of estimated savings on travel to appointments, specialist referrals, unnecessary treatments, hospital stays, routine examinations and partial insurance costs. The resulting benefit-cost ratio was 2.75, meaning each euro spent returned 2.75 euros of assumed benefit, and the net present value, calculated with a 5 percent discount rate, turned positive in the first year and reached approximately 876 euros by year five.

Crucially, the economic result survived stress testing. In a conservative scenario with 20 percent higher costs, 20 percent lower benefits and a 7 percent discount rate, the benefit-cost ratio fell to 1.83 but the net present value remained positive at around 440 euros. Under optimistic assumptions of 20 percent lower costs, 20 percent higher benefits and a 3 percent discount rate, the ratio climbed to 4.13 with a net present value near 1,358 euros. The authors are careful to stress that these figures are scenario-based analytical assumptions, not observed savings, and that healthcare expenditure, reimbursement mechanisms and service-use patterns differ so widely across European countries that the results cannot be directly transferred to any single national system.

The energy assessment delivered perhaps the most striking number in the paper: approximately 0.12 kilowatt-hours per month for all five devices combined. Based on rated power and assumed daily use, the blood pressure monitor and smart ECG monitor, each drawing 5 watts for an assumed 20 minutes per day, dominate the total at 0.05 kilowatt-hours each per month, while the digital thermometer consumes a negligible 0.0025 kilowatt-hours. In practical terms, the entire monitoring suite uses less electricity in a month than a typical household light bulb burns in an afternoon, confirming that direct device-level energy demand is marginal and reinforcing the interpretation of SGAM as an interoperability framework rather than an energy-saving mechanism.

The study is candid about its limitations. The SGAM adaptation is conceptual and has not been validated through prototypes, simulations or real-world deployments with elderly users. The economic figures rest on secondary data and market estimates rather than measured outcomes, the energy calculations rely on assumed operating times rather than observed household consumption, and the analysis covers only five devices out of a rapidly expanding universe of connected medical technology. Potential benefits for autonomy, reassurance, continuity of care and psychological well-being are discussed as expected implications that would require dedicated studies with validated well-being indicators before they can be treated as demonstrated effects.

Even so, the framework arrives at a moment when its questions have become urgent. A SWOT analysis in the paper highlights strengths such as affordable, low-power, user-friendly devices and real-time IoT data, but also weaknesses including data security and privacy concerns, uneven infrastructure across regions, and the fact that connected monitoring suits neither all medical conditions nor all older adults. Cybersecurity threats, rapid technological change and the need to train seniors in digital skills loom as challenges. The authors point to standards-based interoperability, GDPR-compliant data governance and Regulation (EU) 2017/745 on medical devices as the regulatory backbone any deployment would need, and they call for cross-country validation involving elderly participants and healthcare professionals. If future empirical work confirms the promise, the unglamorous architecture that keeps Europe’s lights on may quietly become the scaffold for keeping its oldest citizens healthy at home.

Subject of Research: Integration of IoT-based healthcare devices within the Smart Grid Architecture Model for elderly home care in Europe

Article Title: Integration of IoT-based healthcare devices within the smart grid architecture model: a conceptual framework with economic and energy assessment for elderly care in Europe

Article References: Aghajani, A., Liscio, M. C., Scalise, L., & Sospiro, P. (2026). Integration of IoT-based healthcare devices within the smart grid architecture model: a conceptual framework with economic and energy assessment for elderly care in Europe. Frontiers in Digital Health, 8, Article 1897840. https://doi.org/10.3389/fdgth.2026.1897840

Image Credits: AI Generated

DOI: 10.3389/fdgth.2026.1897840

Keywords: IoT, smart grid, SGAM, elderly care, digital health, remote monitoring, cost-benefit analysis, energy consumption, interoperability, home healthcare, wearable sensors, Europe