Falls Prevention, Clinical Aspects and Technology: Precision Assessment, Predictive Modeling and Digital Health Strategies

Balance control and falls prevention remain significant areas of research within geriatrics, neurology, balance disorders and rehabilitation sciences. As populations age and the burden of conditions such as Parkinson’s disease, Lewy body dementia (LBD), frontotemporal dementia (FTD), vestibular disorder, balance impairment, frailty and long COVID-19 grows, the risk of falls (with their profound physical, psychosocial, and economic consequences) continues to escalate. Emerging evidence highlights how aging and neurocognitive decline deteriorate both static and dynamic balance, especially under dual-task or multitasking conditions, increasing susceptibility to falls. Recent studies demonstrate the intricate interplay between balance impairment and cognitive dysfunction, emphasizing the need for early identification and personalized intervention. Yet, despite advances in our understanding, gaps remain in timely risk prediction, access to specialist care and the integration of technology in routine practice.

Amid these challenges, the field is witnessing an influx of innovative approaches, particularly in predictive analytics and technology-based interventions. Wearable devices, digital assessment platforms, virtual and augmented reality, and telehealth tools are transforming the way clinicians measure balance, predict fall risk, and deliver individualized rehabilitation remotely. Nevertheless, obstacles persist: limited integration into healthcare systems, variable patient adherence to exercise programs, and the complexity of translating predictive models into actionable clinical strategies. Furthermore, while technology shows promise for improving accessibility and proactive intervention, research is needed to enhance understanding of pathophysiology, identify and weight factors, and also evaluate its effectiveness across diverse populations and neurologically complex cohorts.

This Research Topic aims to advance knowledge in balance assessment and falls prevention, with a specific focus on cutting-edge prediction methods and the emerging role of technology. We invite submissions that address the pressing need for more accurate, accessible, and individualized approaches to identifying and mitigating fall risk. Areas of particular interest include the refinement of balance assessment tools, the development and validation of predictive models, and the integration of digital health solutions into multifactorial rehabilitation programs. Additionally, we seek insights into patient and clinician engagement with technology, as well as strategies for enhancing adherence to prevention protocols.

To gather further insights in the evolving landscape of balance assessment and falls prediction, we welcome articles addressing, but not limited to, the following themes:
- Identification of predictive factors for falls
- Predictive models and algorithms for fall risk stratification
- Technology-enhanced balance assessment tools (e.g., wearables, sensor-based systems)
- Integration of artificial intelligence and machine learning in fall prediction
- Digital and remote interventions for falls prevention and rehabilitation
- Neurocognitive and neurological risk factor identification in diverse populations
- Strategies to improve patient adherence and clinician uptake of technology solutions
- Implementation science and healthcare integration of technology-based falls services
- Epidemiological studies for falls supporting need for technological support
- Multidisciplinary approaches to personalized, technology-driven falls prevention

We encourage the submission of original research articles, systematic reviews, meta-analyses, retrospective studies, brief reports, perspectives, case studies, opinion papers and technology evaluations.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Aging, balance assessment, fall prevention and prediction, neurodegenerative diseases, technology-based interventions, wearable devices, cognitive impairment, digital health, rehabilitation

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.