Background: Aging is inherently linked to increased vulnerability to adverse events, with falls being a primary concern for public health. Falls are the leading cause of severe injury and death among individuals over 65 years, significantly impacting their health, quality of life, and mobility 1,2. This growing issue significantly burdens healthcare systems, requiring extensive resources for hospitalization, rehabilitation, and long-term care 3. These challenges require innovative solutions that can effectively mitigate falls risk and improve the safety, independence, and quality of life for older adults while also easing the strain on healthcare systems 4.
Several factors increase the risk of falls in older adults, including aging, low muscle mass due to physical inactivity, smoking, alcohol consumption, and malnutrition 5,6. Comorbidities such as diabetes, hearing loss, cognitive decline, poor sleep, chronic pain, and depression further exacerbate falls risk, leading to frailty and mobility limitations 7. Additionally, many older individuals live alone, which increases the likelihood of delayed help after a fall 8.
Declines in physical, cognitive, and sensory functions significantly raise fall risk, making it increasingly important to identify innovative solutions to prevent falls and promote healthy aging 9. As Professor Bernard Isaacs (1924-1995) remarked, "A child takes one year to acquire independent movement and ten years to acquire independent mobility. An older adult can lose both in a single day." This highlights the potentially fatal consequences of falls, which can severely compromise mobility and independence 2. Recent technological advancements have introduced valuable tools for geriatric healthcare, offering solutions that improve quality of life, promote autonomy, and optimize healthcare delivery for older individuals 10.
Technologies such as wearable devices (smartwatches and health sensors), telemedicine, robotic assistance, and health management systems play a central role in monitoring vital signs, facilitating early detection of health issues, and enabling remote care 11,12 In addition, technologies for rehabilitation, such as exoskeletons and exercise applications, provide support for physical activity, aiding in the prevention of falls and enhancing recovery 13,14. Building on the advent of the Internet of Things and Artificial Intelligence, the spectrum of possible solutions and applications continues to grow, with increasing the potential to monitor and predict fall risk and to enhance rehabilitation procedures 15.
Despite these advances, challenges remain, including the accessibility of technologies, their integration into existing care systems, and the need for personalized solutions 16. The lack of coordination among various technological platforms can reduce efficiency and complicate the interpretation of data, while cost and usability can limit access for many individuals 17. Moreover, ensuring that non-pharmacological interventions (e.g. exercise-based technology) are tailored to the unique needs of each individual is essential for long-term success 18.
The search for innovative solutions in public health is driven by the growing challenges of the aging population, particularly in fall prevention, chronic disease management, and overall well-being 19. As life expectancy increases, the prevalence of debilitating conditions also rises, requiring sophisticated and personalized approaches 20. Innovations such as wearable devices and remote monitoring systems offer the potential to enhance care quality, by enabling more precise and effective interventions. However, their adoption depends on accessibility, economic feasibility, and ease of use—especially for older adults with varying levels of digital literacy 21.
The effectiveness of these solutions must be rigorously validated through dedicated studies to ensure improved health outcomes and reduced long-term healthcare costs. Effective technologies can prevent unnecessary complications and hospitalizations, while personalized interventions, based on precise data and continuous monitoring, address the specific needs of each individual 20,22. Digital technologies can offer accessible, customized solutions that overcome the limitations of traditional approaches, promoting safer and healthier aging 23.
Collaboration between healthcare professionals, developers, and final users' contributions may also be important to ensure that solutions are relevant, effective, and sustainable in the long term 24. Identifying and addressing limitations during the research and development process can reduce barriers to adoption and improve implementation, increasing the likelihood that fall prevention technologies will be successfully integrated into care strategies for the aging population 9.
Given these challenges, there is a growing need for innovative and sustainable solutions that address the multifaceted nature of fall prevention. This study aims to lay the foundation for the development of a web-based medical device and app platform that integrates digital technologies, physical-functional assessments, and user-centered approaches to predict and prevent falls in older adults. Objective: To explore, evaluate, and establish the foundation for Stage 1 of the Sustainable and Accessible Fall-Prevention Medical Device Technologies for Older Adults’ Health (S@FHe-RP).
Research Questions
I. What digital medical device technologies are currently available for predicting fall risk in older adults, and how do they compare in terms of functionality, usability, and effectiveness?
II. Which physical-functional assessments are commonly used to evaluate fall risk in older adults, and how can these be adapted or enhanced through medical device–based digital technologies?
III. How do end users, including healthcare professionals and nursing home staff, perceive the acceptability, feasibility, and functionalities of fall-prevention medical devices?
IV. Which bio-socio-demographic factors are essential for developing a reliable and accurate medical device–driven fall prediction model in long-term care facilities?
V. How can the integration of multidisciplinary and technological approaches, including medical device innovation, overcome the limitations of current fall-prevention strategies and support the development of sustainable solutions in long-term care facilities? Methods: This research protocol is structured into four interconnected studies (Stage 1–Stage 4), each designed to systematically align the research process with the overarching objectives of the project. Stage 1 involves a scoping review (Study 1) to identify and map existing digital technologies used to predict fall risk in older adults. Stage 2 (Study 2) focuses on a comprehensive systematic review comparing traditional and technology-adapted physical fitness assessment tests. Stage 3 (Study 3) explores the development of a statistical prediction model based on bio-sociodemographic indicators to estimate fall risk. Finally, Stage 4 (Study 4) integrates the end-users’ perspectives—health professionals and caregivers in long-term care settings—to inform the design and implementation of a sustainable, user-centered fall-prevention technology.
Together, these four stages create a cohesive framework that bridges evidence synthesis, model development, and practical application, ensuring scientific rigor and real-world relevance. The methodology follows recent approaches 25,26, integrating technological, clinical, and user-centered dimensions.
Project Design
A mixed-methods approach will be employed, comprising four complementary and consecutive studies to support the development of a low-cost, web-based app platform for fall prevention in older adults. This platform will comply with European medical device regulations, meeting validation criteria for medical devices 27. The research characterizes contemporary translational research, which involves effectively translating scientific knowledge into new health technologies 28. Furthermore, the development of this medical device will involve researchers from distinct areas of clinical, technological, and industrial sectors, following a User-Centered Design (UCD) approach 29.
Figure 1.
Module A: Scoping review of new digital technologies to predict falls
This phase involves a systematic scoping review of the literature across relevant databases, specialized journals, and grey literature, aiming to analyze studies examining new digital thecnologies used to predict fall risk in older adults. This study will adhere to established methodological guidelines, including the exploratory review framework by Arksey and O'Malley (as refined by 30), and the Joanna Briggs Institute (JBI) guidelines 31. Results will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines 32.
A reproducible four-step search strategy, as recommended by JBI 33, will be implemented across databases (see Table 1): Medline, Web of Science, SCOPUS, Cochrane Library, B-On, CINAHL, PsycINFO, Google Scholar, and Open Access Thesis and Dissertations. The search terms included: ((Falls prevention) OR (Fall risk)) AND (Elderly) AND (Digital technologies) AND (Long-term care)). The inclusion criteria, guided by the Joana Brigs framework 34, will include studies involving older adults (≥60 years) (Population), examining technological devices assessing fall risks related to gait performance (Concept), across various settings, countries, and sectors of the economy (Context).
Two reviewers will design the strategy, which will be reviewed by a third expert using the PRESS checklist 35. Selection criteria will include original research, systematic reviews, theses, grey literature, patents and prototypes from the last ten years. Quantitative studies (randomized controlled trials, non-randomized trials, quasi-experimental studies) and observational studies (descriptive, exploratory, and analytical designs) will be considered. The initial search will be conducted in English, and may be expanded to include Spanish and Portuguese depending on preliminary findings. Findings will summarize studies by PCC, answer research questions, and describe study characteristics.
Module B: Review of Traditional and Tech-Adapted Physical Fitness Tests
This comprehensive review will identify traditional and technology-adapted physical-functional tests (PFT) for fall risk assessment in older adults. The review will focus on the effectiveness, ease of use, accuracy, and implementation challenges. A systematic search across multiple databases (Medline via PubMed, Scopus, Web of Science, CINAHL) will target studies from the last decade using terms such as “fall risk assessment,” “functional test,” “older adults,” “physical test,” “digital adaptations,” and “technology for fall prevention.” Inclusion criteria include original research, systematic reviews, and relevant grey literature.
Results will follow PRISMA guidelines 36, Cochrane guidelines for systematic reviews, and the GRADE methodology for evaluating the quality of evidence 37. The PICO framework 38 will structure reporting: Population (≥ 60 years, including clinical subpopulations), Intervention (traditional and adapted PFTs), Comparison (traditional vs. digital/technological adaptations), and Outcome (effectiveness on fall risk assessment).
Module C: Fall risk prediction statistical model
This cross-sectional study is aimed to develop a predictive algorithm for fall risk based on bio-socio-demographic indicators. This study will involve approximately 300 institutionalized older adults from Coimbra, Portugal, levering data from the PRO-HMESCI project 39 project. Participants will provided informed consent, with ethical approval obtained from the Faculty of Sports Sciences and Physical Education Ethical Committee, University of Coimbra (code number: CE/FECDEFUC/0002013, CE/FCDEF-UC/00112024).
Collected data includes sociodemographic data (age, marital status, education), anthropometric measures (weight, height, BMI), cognitive status 40, state of depression 41, comorbidity index 42, functional fitness (e.g. upper and lower body muscle strength and balance), and falls history.
Advanced statistical techniques will be applied to identify significant patterns and relationships between bio-socio-demographic indicators and risk of falls in older adults. Bivariate analysis methods, such as the chi-square test and Pearson's correlation coefficient, will be used to explore associations between categorical and continuous variables. Multivariate logistic regression will be applied to identify the most significant factors contributing to fall risk 43 and construct the predictive model. Model validation will be performed using cross-validation techniques and Receiver Operating Characteristic (ROC) curve analysis to assess the accuracy and predictive power of the algorithm 44.
Module D: End-Users' Perspective study
The objective of this study is to validate the necessity, feasibility, and acceptance of a technology-based fall prevention product by healthcare professionals working in long-term care facilities. This study will adopt a mixed-methods approach, combining qualitative and quantitative techniques to evaluate the acceptance and perceived utility of a new technology-based solution for fall prevention in older persons in care facilities. It seeks to understand user perspectives, barriers, and facilitators, as well as institutional specificities related to long-term care, based on the UCD methodology 45.
The adapted Technology Acceptance Model (TAM) serves as the theoretical framework connecting the study's qualitative and quantitative assessement 46. Insights gathered through qualitative interviews will inform the contextual understanding of perceived usefulness, ease of use, and behavioral intention, which are central to the TAM framework 46. These constructs guide the design and evaluation of fall prevention technologies, as visualized in Figure 2, ensuring alignment with user needs and practical applications.
Participants include higher-education healthcare professionals (nurses, physicians, physiotherapists, gerontologists) with at least one year experience in long-term care. For the qualitative phase, theoretical saturation is expected to occur after 15–20 interviews, following similar previous studies 47. For the quantitative phase, the sample size will be calculated using G*Power, based on the sample size as shown in previous studies 48. Qualitative analysis will employ thematic content analysis using NVivo® 49; quantitative analysis will employ TAM-based questionnaire to evaluate variables such as perceived usefulness (10 items), perceived ease of use (11 items), behavioral intention (4 items), and demographic data (6 items) 50. Data collection will occur via online and in-person distribution, with follow-up to ensure response completeness. Descriptive and inferential statistical analyses will be conducted using SPSS.
Ethical Procedures
The research protocol was approved by the same committee under the project number CE/FCDEF-UC/00112024, which ensures compliance with ethical standards for research involving human participants. Procedures will comply with the European Union directives 51, and relevant guidelines from the International Organization for Standardization 52. Clinical studies will also adhere to the ethical principles outlined in the Declaration of Helsinki and the guidelines for Good Clinical Practice 53. As we are currently in Stage 1 of the project, the Portuguese regulatory body responsible for overseeing the evaluation authorization, and monitoring of medicines, medical devices, and health products in Portugal 54 approval is not required at this point, but it will be necessary in Stage 2 when developing and validating the platform prototype. Results: The study aims to significantly advance fall risk prevention by integrating traditional and technological approaches. A key outcome will be a comprehensive analysis of PFTs, both conventional and technology-adapted, to identify the most effective methods for assessing fall risk. By systematically comparing these approaches, the study will bridge the gap between traditional clinical assessments and emerging technologies 55, providing a roadmap for integrating innovative tools into routine practices and highlighting their complementary potential.
Additionally, the study will support the development and preliminary validation of low-cost, data-driven algorithm to predict fall risk in older adults. By combining bio-sociodemographic, clinical, and functional data, this tool will enhance clinical decision-making and enable timely , targeted interventions 56, with a strong emphasis on early screening and fall prevention 57.
The evaluation of existing fall risk assessment technologies will also play a central role, aiming to verify their reliability and accuracy. This assessment will help to identify strengths and limitations, guide future development, and establish benchmarks for next-generation tools. A particular focus on gait performance and fall risk will contribute to ongoing advancements in smart wearable devices and motion analysis technologies 14.
Moreover, the study will capture end-user perspectives, particularly from healthcare professionals working in long-term care facilities. It will explore factors influencing the adoption of fall prevention technologies, including barriers, facilitators, and overall acceptability. Applying the UCD approach will ensure that future solutions align with the real-world needs and preferences of those who use them 58.
Finally, this work will establish a solid foundation for developing a web-based medical device for fall-risk management. Following evidence generation and ethical ethical approval, the next phase will involve rigorous prototype testing to ensure safety, efficacy, and compliance with international standards, including EU and ISO guidelines 51. Conclusions: The research protocol aims to provide valuable insights into the development and adoption of fall prevention technologies for older adults using a user-centered design approach. By focusing on low-cost, sustainable, and accessible solutions, the study addresses the pressing need for equitable healthcare innovations—particularly in underserved and resource-limited contexts. The findings will contribute to enhancing fall prevention strategies and ensuring that future technologies are not only clinically effective but also aligned with real-world needs and preferences of their users.
