Falls are a significant and rapidly growing public health problem. It is estimated that 37.3 million falls require medical attention and more than 684,000 fall-related deaths occur globally each year.1 Falls occur across the lifespan, but they are a well-documented issue for older adults, and the rising rate of falls is associated with the aging population.1 Falls occur in a range of settings, and there is a substantial body of evidence investigating fall prevention and detection in both community-dwelling2 adults and, as reported in this issue of JBI Evidence Synthesis, in adult hospital inpatients.3 However, there is still work to be done in this area, with the World Health Organization earlier this year calling for fall prevention and management to be pushed “higher up the planning, policy, research and practice agenda… to reduce the burden… on a local and global scale.”4(p.vii)
A broad range of health technologies (defined as the application of organized knowledge and skills in various forms, eg, exercise, quality improvement strategies, assistive technology) have been implemented in attempts to reduce falls and to detect them in a timely manner when they do occur in the community,2 care,5 and hospital settings.3 Over the previous decade there has been increased focus on SMART (self-monitoring, analysis, and reporting technologies) technologies in both fall detection and prevention. While there appears to be great enthusiasm for, and development of, SMART technologies and the use of data in general to address falls in adults, many challenges remain. Further research and evidence synthesis are required before SMART technologies that are evaluated in small-scale research studies can be implemented extensively in real-world settings and contribute to a reduction in falls and their associated harms.
In our scoping review,3 we mapped the technologies being used or developed for fall prevention or detection in adult hospital inpatients. SMART health technology was predominantly reported for fall detection devices, such as fixed or wearable pressure or movement sensors and depth cameras. Such devices were the second-most-frequently reported technology after various combinations of health technologies with or without fall risk assessment (ie, multifactorial or multicomponent interventions). However, over two-thirds of reports on these devices were categorized as “emerging technologies,” meaning they were conducted in laboratory-based or mock clinical settings rather than the real-world hospital environment, with many reports focusing on descriptive accounts of the SMART technology and its performance, and not on robust evaluation of their effectiveness for detecting falls. While it is encouraging that such SMART technologies are being developed, as long as the technology readiness levels remain low and issues of acceptability, feasibility, cost, and barriers to use remain unaddressed,6 many will not progress to widespread adoption, and their potential to influence this important public health issue will not be realized.
Our research group has also recently conducted a review7 identifying over 60 SMART technologies for fall prevention in the community setting alone, including a plethora of digital health applications (mobile apps) aimed at maintaining or increasing physical activity and preventing frailty; a range of fixed and wearable sensors; and a handful of technologies using artificial intelligence to predict the risk of falls and enable early intervention. Such technologies have clear potential in terms of personal benefit to those most at risk of falls, and economic benefits to health care services if even a proportion of falls and their consequences, including hospital admission, can be prevented.
However, there are a number of limitations associated with the use of SMART technologies and challenges in their widespread adoption. For example, many wearable SMART technologies must be worn for lengthy durations, which can be inconvenient or uncomfortable for individuals. Many operate via devices that require charging, and those that require a mobile phone to operate also require syncing and the ability to navigate a mobile app.7 Additionally, some technologies produce a large volume of data, which may overwhelm individuals and their carers. As with any health promotion intervention, behavior change is required for optimal engagement and outcomes. Research demonstrates that individuals tend to disengage from SMART technologies for self-monitoring after only four months,8 and “alarm fatigue” is a well-documented barrier to the effectiveness of bed exit alarms in hospitals.9 Technology alone will not solve the growing issue of falls; a cultural change among individuals, health care workers, and services will be required for technology to reach its full potential. SMART technologies have clear potential for the prevention and detection of falls in adults, and future global collaborative research must focus on addressing the challenges identified, particularly moving research to the real-world setting and addressing barriers to widespread adoption.
1. World Health Organization. Falls fact sheet [internet]. Geneva: World Health Organization; 2021 [cited 2021 Aug 19]. Available from: https://www.who.int/news-room/fact-sheets/detail/falls
2. Dautzenberg L, Beglinger S, Tsokani S, Zevgiti S, Raijmann RCMA, Rodondi N, et al. Interventions for preventing falls and fall-related fractures in community-dwelling older adults: a systematic review and network meta-analysis. J Am Geriatr Soc 2021.
3. Cooper K, Pavlova A, Greig L, Swinton P, Kirkpatrick P, Mitchelhill F, et al. Health technologies for the prevention and detection of falls in adult hospital inpatients: a scoping review. JBI Evid Synth 2021;19 (10):2478–2658.
4. World Health Organization. Step safely: strategies for preventing and managing falls across the life-course. Geneva: World Health Organization; 2021 [cited 2021 Aug 19]. Available from: https://www.who.int/publications/i/item/978924002191-4
5. Francis-Coad J, Etherton-Beer C, Burton E, Naseri C, Hill A-M. Effectiveness of complex falls prevention interventions in residential aged care settings: a systematic review. JBI Database System Rev Implement Rep 2018;16 (4):973–1002.
6. Lapierre N, Neubauer N, Miguel-Cruz A, Rios Rincon A, Liu L, Rousseau J. The state of knowledge on technologies and their use for fall detection: a scoping review. Int J Med Inform 2018;111:58–71.
7. Cooper K, Burnett V, Harrison I, Moss R, Cooper R, Myint PK, et al.
Independent evaluation of ARMED anti-fall solution: Landscape Review Report 2020. Report submitted to Digital Health and Care Innovation Centre (Scotland).
8. Chaudhry UAR, Wahlich C, Fortescue R, Cook DG, Knightly R, Harris T. The effects of step-count monitoring interventions on physical activity: systematic review and meta-analysis of community-based randomised controlled trials in adults. Int J Behav Nutr Phys Act 2020;17 (1):129.
9. Seow JP, Chua TL, Alowent F, Lim S, Ang SY. Effectiveness of an integrated three-mode bed exit alarm system in reducing inpatient falls within an acute care setting. Jpn J Nurs Sci 2021; e12446.