Mobile Technology for Falls Prevention in Older Adults

Abstract

Falls are the leading cause of accidental death in older adults that result from a complex interplay of risk factors. Recently, the need for person-centered approach utilizing personalization, prediction, prevention, and participation, known as the P4 model, in fall prevention has been highlighted. Features of mobile technology make it a suitable technological infrastructure to employ such an approach. This narrative review aims to review the evidence for using mobile technology for personalized fall risk assessment and prevention since 2017 in older adults. We aim to identify lessons learned and future directions for using mobile technology as a fall risk assessment and prevention tool. Articles were searched in PubMed and Web of Science with search terms related to older adults, mobile technology, and falls prevention. A total of 23 articles were included. Articles were identified as those examining aspects of the P4 model including prediction (measurement of fall risk), personalization (usability), prevention, and participation. Mobile technology appears to be comparable to gold-standard technology in measuring well-known fall risk factors including static and dynamic balance. Seven applications were developed to measure different fall risk factors and tested for personalization, and/or participation aspects, and 4 were integrated into a falls prevention program. Mobile health technology offers an innovative solution to provide tailored fall risk screening, prediction, and participation. Future studies should incorporate multiple, objective fall risk measures and implement them in community settings to determine if mobile technology can offer tailored and scalable interventions.

One in four community-dwelling adults aged 65 years and older fall annually (1). Falls can result in serious health consequences, including fractures, traumatic brain injury, and even death (2,3). Indeed, they are the leading cause of accidental death and injury in older adults (4) accounting for 95% of hip fractures (5) and 80% of traumatic brain injuries (6). As such, falls account for ~1% of health care expenditures in developed countries (7). In addition to physical injuries and financial impact, falls frequently have a psychosocial impact including an increase in fear of falling and activity curtailment, which subsequently leads to further physiological decline and ultimately a loss of independence (8,9). Preventing falls and fall-related injuries is critical to maintaining independence and quality of life in older adults.

Due to the importance of falls, over the last several decades, the scientific community has investigated numerous approaches to prevent falls. Over 159 000 older adults have participated in clinical trials focusing on fall prevention (10). This collective research has confirmed that falls are a serious problem resulting from a complex interplay of risk factors (11). This includes physiological (ie, balance, leg strength) (12), psychosocial (ie, fear of falling, mental well-being) (13), pharmacological (ie, number and type of medications) (14), cognitive (ie, processing speed, executive function) (15), and environmental (ie, uneven surfaces, tripping hazards) risk factors (16). The increase in the understanding of falls has led to interventions that target 1 or multiple risk factors; however, the implementation of evidence-based fall prevention programs has had limited success in the community (10), and the age-adjusted fall death rate among older adults has nearly doubled in the last decade (17). This dramatic increase in death rate, along with the recent null result of a well-powered pragmatic fall prevention trial (18), highlights the need for innovative approaches. Indeed, a global steering committee from the World Congress on Falls and Postural Instability recently published guidelines to achieve evidence- and consensus-based fall prevention recommendations (19). Their goal is to incorporate personalization, prediction, prevention, and participation into an assessment and management algorithm (ie, P4 model). This P4 model offers a novel approach to optimize and tailor falls prevention care to older adults.

Unfortunately, numerous barriers to implementation of fall prevention exist. For instance, data suggest that less than 20% of physicians screen older adults for fall risk and make appropriate treatment plans (20). Even when physicians are systematically encouraged to objectively measure fall risk (with external funding and best practices in place), over 75% report that time constraints and competing medical priorities are significant barriers to implementing fall screening in a clinical setting (21). Moreover, clinicians solely relying on self-report miss critical opportunities for effective intervention (22,23). Data indicate that combination of objective measures of mobility combined with patient-reported outcomes offers superior assessment of fall risk (24). The development and refinement of technology over the last 2 decades offer innovative solutions to implement the P4 model of fall prevention. Indeed, an alternate approach that would address these critical barriers and maximize access to fall screening and prevention is to utilize commercially available technology. Mobile technology, defined as portable, communication technology enabled with internet (25) (eg, smartphones, tablets), is relatively affordable and ubiquitous (in developed countries), making this tool an ideal infrastructure for fall risk screening and prevention. Although adoption of technology is often lower in older adults, data from the Pew Research Center indicate that nearly 70% of “baby boomers” and 40% of the “silent generation” own a smartphone or tablet within the United States (26).

Several features of mobile technologies make them suitable for monitoring fall risk. Mobile devices can be designed to maximize user interface allowing for the collection of numerous patient-reported fall-related risk factors such as health history, medication use, and important perceptual outcomes including fear of falling and activity curtailment. Additionally, a significant portion of older adults are familiar with their use and have access to them (26). Lastly, inertial measurement units (IMUs) embedded within mobile technology have been leveraged to measure movement, including gait and balance which are strongly related to fall risk (27). With the features of mobile technology, it is possible to incorporate the P4 model and integrate personalization, prediction, prevention, and participation to optimize falls prevention (Figure 1).

Figure 1.

Mobile health technology as a tool for personalized fall risk assessment, prevention, and participation. (A) Older adults may be concerned and contemplate falls for themselves and loved ones; (B) older adults utilize mobile health technology outside of healthcare settings to quantify their individualized fall risk; (C) older adults are provided personalized, actionable recommendations and local resources for fall prevention.

In 2017, a systematic review evaluated mobile device applications that measure balance and fall risk in older adults (28). A total of 13 publications including approximately 125 older adults were included in the review. It was found that the majority of work did not quantify validity of the approaches nor the usability of mobile technology (28). The majority of applications were intended to be used by clinicians and not older adults themselves. Although the review has been well received, it is quickly becoming “dated” given the growing popularity and use of mobile health technology. Consequently, the purpose of this narrative review is to provide an update on the evidence for mobile technology for fall risk assessment and prevention within the context of the P4 model in order to identify lessons learned and future directions for falls prevention.

Method

The following search strategy was used in PubMed and Web of Science: mobile technology, smartphone, or tablet; fall, fall risk, fall prevention; older adults, elderly, aging. Articles were searched from 2017 to November 2021. Articles were included if they used a type of mobile technology, measured at least 1 fall risk factor, and included adults 50 years or older. Articles that exclusively examined fall detection were excluded. Importantly, this review will not focus on wearables—although they have significant promise in passively monitoring fall risk in older adults they are a distinct technology outside the scope of this review (29). A total of 23 papers were included in this review.

Results

Fall Risk Assessment

Mobile technology validation

Since 2017, a number of studies have focused on quantifying static and dynamic balance—leading fall risk factors—with mobile technology IMUs, including smartphones and tablets. Mobile technology is embedded with an accelerometer, gyroscope, and magnetometer, similar to IMUs used in wearable sensors to measure mobility. Four studies evaluated the validity of a smartphone IMU in measuring static balance across a range of motor tasks (see Table 1) (30,32,34,38). For instance, De Groote et al. (30) and Hsieh et al. (32) both compared smartphone IMU to force plates, the biomechanical gold standard for measuring postural control. Both studies analyzed conditions that challenge the base of support (ie, semi-tandem and tandem stances), visual availability (eg, eyes open/closed), and cognitive demand (eg, dual-task conditions). While De Groote placed the smartphone at the lower back, Hsieh placed the smartphone near the sternum. Both studies found moderate to strong correlations between measures derived from the smartphone IMU and from the force plate, with stronger correlations between devices during more challenging conditions—when there were greater amounts of postural sway. Mansson et al. (34) and Sampaio et al. (38) also used a smartphone IMU to analyze static balance tasks and compared to clinical balance tests. Mansson et al. (34) extracted several IMU measures in the time and frequency domains, and found weak to moderate correlations between the smartphone IMU and clinical tests in 31 older adults. Sampaio et al. (38) found that in a sample of 54 older adults standing balance performance from a smartphone IMU was not able to differentiate between low-, medium-, and high-risk fallers as classified by performance on the Timed Up and Go. However, the measures extracted from the IMU to quantify balance were not detailed within the report.

Table 1.

Description of Studies That Used Mobile Technology to Quantity Static or Dynamic Balance

Study	Sample Size	Age Mean (SD)	Location	Task	Comparison	Smartphone Location	Main Outcomes
De Groote et al. (30)	97	Between 50 and 90 years	Belgium	Static balance: eyes open, closed, semi- tandem, dual-task	Force plate	Lower back, S2	Low to moderate correlations (winsorized correlation = 0.14–0.82) Moderate test–retest reliability (ICC = 0.5–0.75)
Gerhardy et al. (31)	41	72.6 (7.5) years	Germany	Timed Up and Go	Performance during standing balance tasks measured by inertial measurement unit	Lower back	Weak to moderate correlations (r = 0.19–0.56)
Hsieh et al. (32)	30	65.9 (8.8) years	United States	Static balance: eyes open, closed, dual-task semi-tandem, tandem, single leg	Force plate	Medially against the chest along the sternum	Weak to strong correlations (rho = 0.13–0.81) Distinguished between those at low and high fall risk (AUC = 0.66–0.84)
Kuntapun et al. (33)	12	75.6 (5.6) years	Thailand	Walking on level and irregular surfaces and over obstacles	Motion capture; video camera	Lower back, L3	High correlations for spatiotemporal gait measures (r = 0.81–1.0) Excellent reliability (ICC = 0.94–0.99)
Mansson et al. (34)	31	78.7 (4.7) years	Sweden	Static balance: feet together, semi-tandem 3× sit to stand	Clinical balance tasks: Mini-BESTest, Functional Reach, Modified 4-stage Balance Test, Modified Stepping Test Clinical strength tasks: 5× sit to stand, 30-s chair stand, 1 max leg press	Lower back, L4–L5	Weak to moderate correlations with clinical balance tasks (rho = −0.13 to 0.67) Weak to moderate correlations with clinical leg strength tasks (rho = 0.24–0.64)
Marques et al. (35)	40	78.9 (8.6) years	Portugal	Single sit to stand	Video camera	Waist	High accuracy (96.1%) and low percent error (0.05) in determining sit-to-stand time Excellent reliability (ICC = 0.92–0.97)
Orange et al. (36)	27	72.3 (7.4) years	United Kingdom	Single sit to stand	Motion capture	Tripod 3 m from participant	High correlations for sit-to-stand velocity (r = 0.94) and power (r = 0.74)
Oshima et al. (37)	66	73.7 (6.1) years	Japan	Preferred and slow walking speeds	OptoGait	Tripod 1 m from start line	Good to excellent agreement (ICC = 078–0.97)
Sampaio et al. (38)	54	71.3 (7.4) years	Brazil	Static balance: eyes open	Timed Up and Go, Performance-oriented Mobility Assessment	Lower back, S1–S2	App underestimated low-risk fallers overestimated high-risk fallers based on TUG and POMA. Did not classify moderate risk fallers.
Steinert et al. (39)	44	73.9 (6.0) years	Germany	Preferred and fast walking speed	GAITrite and Microsoft Kinect Sensor	170 cm away from GAITrite mat	Weak correlation for gait speed compared to GAITrite (r = 0.28); weak to moderate correlation for step length and step time (r = 0.16–0.41)

Notes: AUC = area under the curve; ICC = intraclass coefficient; POMA = performance-oriented mobility assessment; SD = standard deviation; TUG = Timed Up and Go.

In addition to static balance tasks, 6 studies examined how a smartphone IMU can quantify dynamic balance (31,33,35–37,39). These studies examined motor tasks believed to be related to fall risk including sit to stands, gait, and the Timed Up and Go. The sit-to-stand task as measured by smartphone IMU was compared with optical motion capture (36), video camera (35), and clinical leg strength tasks (34). Smartphone-derived measures during the sit to stand were also moderately correlated with clinical leg strength tasks (ie, leg press and maximal stepping in a sample of 31 older adults (34)) and comparable in estimating the time it takes to stand up and sit down as quantified by video analysis (35). In addition to sit to stands, other studies have also examined dynamic balance as a fall risk factor through walking tasks. Gerhardy et al. (31) measured the Timed Up and Go with a smartphone and found that subphases of the Timed Up and Go may help screen for somatosensory deficits in older adults. In another study, participants walked over level and irregular surfaces while having a mobile phone strapped to the lower back (33). The spatiotemporal gait measures derived from the smartphone were found to be highly correlated with measures derived from traditional motion capture approaches. This study, however, included small sample of older adults (n = 12) and an ecological irrelevant location for the mobile phone. Another study compared a smartphone camera, rather than the IMU, to a pressure walkway to measure gait speed and spatiotemporal measures and found weak to moderate correlations (39). Collectively, these studies provide preliminary evidence that smartphone IMUs are capable of accurately measuring dynamic movements in older adults, although limited samples preclude firm conclusions.

In addition to validating the ability to quantify static and dynamic movements, 3 of these studies also examined reliability and found moderate to excellent test–retest reliability (30,33,35). These results provide preliminary evidence that measuring static and dynamic balance with smartphone-based IMUs is reliable over time.

IMUs appear to be the most leveraged tool within smartphones to measure static and dynamic balance. Three studies, though, utilized a smartphone’s camera to capture 2-dimensional motion during dynamic balance tasks (36,37,39). These studies used smartphone’s camera to record dynamic movements and compared them to motion capture cameras (36), the GAITrite pressure mat (39), and OptoGait camera system (37). These studies show mixed results, with weak correlations in measuring gait speed and cadence compared to the GAITrite (39), and stronger correlations in measuring sit-to-stand velocity (36). It appears that smartphone IMUs may be more accurate in quantifying dynamic balance than smartphone cameras.

These 10 studies add to the evidence base that smartphone IMUs can quantify static and dynamic measures of balance and as such are relevant to the prediction component of the P4 model. However, it should be noted that many of these studies had small sample sizes, varying smartphone placements on the body, and extracted different acceleration measures to quantity balance. Future studies should include larger samples of older adults with a range in mobility function and determine if smartphone location influences accuracy of objective assessment of balance.

Development and usability

Over the last few years, mobile health applications (apps) have been developed for older adults to measure their unique fall risk. Table 2 depicts mobile health applications that quantify different fall risk factors. Five of the apps (31,41,42,45,46) include measuring static or dynamic balance using a smartphone IMU, while 4 of the apps (40–42,47) recorded self-reported fall history. Five of the apps (40–42,45,47) include multiple risk factors while 2 of the apps (31,46) only measure a single risk factor. The majority of the apps measure fall risk based on participant-reported outcomes. Only 2 of the apps provide suggestions for fall mitigation strategies. Taheri-Kharameh et al. (45) included educational resources and the Otago program for exercises and Greene et al. (40) included advice and video-based exercises. It is unclear how these apps tailored their exercises and educational resources based on fall risk stratification. The potential ability to tailor mitigation strategies to an individual’s risk profile, that is prevention within the P4 model, seems to be a potential strength of mobile technology warranting further investigation.

Table 2.

Fall Risk Applications Developed and Fall Risk Factors Measured in Each Application

Study	Research Question	Risk Factors
		Participant-Reported Outcomes							Objective Outcomes
		Age	Fall History	Vision/Hearing	Polypharmacy	Fear of Falling	Cognition	Environment	Static Balance	Dynamic Balance
Greene et al. (40)	Algorithm to discriminate fallers and nonfallers; implementation		X	X	X				X
Rabe et al. (41)	Algorithm to discriminate fallers and nonfallers		X	X	X	X	X	X		X
Hsieh et al. (42)	Usability	X	X			X			X	X
Hsieh et al. (43)	Algorithm accuracy; implementation	X	X			X			X	X
Mansson et al. (44)	Usability								X	X
Taheri-Kharameh et al. (45)	Usability	X								X
Zhong and Rau (46)	Usability									X
Rasche et al. (47)	Acceptability		X	X		X		X	X

A critical step in the development and ultimate implementation and adoption of health apps is to determine usability and ensure that the intended users can easily use the app. Seven fall risk apps were published since 2017, and 4 of those apps were tested for usability (42,44–46). Usability testing approaches included thinking aloud, in which participants think their thoughts aloud as they are using the app, interviews asking about likes and dislikes, and questionnaires on a standardized scale such as the System Usability Scale. While each study identified areas to improve the usability of app, only 1 study conducted a second iteration to determine if usability improved (42). Usability remains an important component to ensure ease of use and value (48), but these results demonstrate that few studies incorporate user-centered approaches when developing fall risk apps. Given the unique usability needs of older adults, future research should continue to perform usability testing and conduct multiple iterations to ensure usability and acceptability in diverse populations.

Testing in real-world environments

A major benefit of mobile health technology is the potential to implement them into the community. Because of the relatively affordability and ubiquity of mobile devices, there is substantial potential for fall risk apps to offer tailored screening and prevention outside of traditional health care settings. Despite this promise, only a limited amount of fall risk apps discussed in the literature (n = 3) were tested outside of research/health care settings (40,43,47). Two of the studies created and tested algorithms that incorporate multiple risk factors to determine overall fall risk. Hsieh et al. (43) tested their fall risk app in a sample of 15 older adults in a retirement community, and their algorithm measuring fall history, balance, balance confidence, and fear of falling was moderately to strongly correlated with clinical fall risk assessments. Greene et al. (40) tested their app in the real world in a sample of 594 assessments across 147 unique smartphones, and measured fall risk based on balance, polypharmacy, mobility problems, and vision problems. Their algorithm strongly correlated with fall history and classified nonfallers, fallers, and recurrent falls with 62% accuracy. Rasche et al. (47) performed field testing with their app, counting the number of downloads and determining overall acceptability with their app.

While these studies offer promise in implementing mobile health fall risk assessment into community settings, very few of the developed fall risk apps have been tested or utilized outside of laboratory settings. Our search of the literature suggests that mobile health apps have primarily focused on validation, some have included usability testing, and few have implemented fall risk apps in real-world settings.

Commercial Fall Risk Apps

Within commercially available health apps, Apple released an iPhone feature in 2021 that measures gait patterns and alerts users who are at risk of falling. However, the scientific evidence supporting Apple’s fall risk feature is unclear. Based on press releases and media reports (49), it appears that the application quantifies walking asymmetry and alerts users and/or caregivers and clinicians whether they are outside a normal range as defined by the app. Although gait asymmetry is related to fall risk (50), the validity of this approach is not well documented.

The Lockhart Monitor is another commercially available application that reports to measure fall risk by quantifying gait, balance, and balance confidence (51). The Lockhart Monitor reports outputs derived from a smartphone’s IMU including gait speed, sway area, and sway path. Based on these scores, the app sends a fall risk alert. However, the validity and reliability of the Lockhart Monitor have not been reported in the literature. Safesteps also developed a falls prevention app that measures 12 fall risk factors to identify those at high risk for falls (52). However, the scientific evidence supporting this app is not clear.

Fall Prevention Interventions

The fall risk apps previously described focused on measuring fall risk factors in older adults. Another potential of mobile technology is to integrate apps into clinical interventions—the “participation” component in the P4 model. Hawley-Hague et al. (53), for instance, developed a smartphone app that offers personalized exercises to reduce older adults’ risk of falls. They also included behavior change strategies, such as goal setting and action planning, to increase adherence to the exercises. Four other studies used mobile technology as a tool to deliver exercises to prevent falls (54–57). StandingTall delivers balance exercises over a tablet and was tested with a falls prevention clinical trial, and demonstrated high acceptability and usability among 50 older adults (56,58). KOKU is a gamified balance and strength app that was delivered to 28 participants and was found to be acceptable and feasible (57). Additionally, 2 studies used mobile technology to first assess for fall risk and then provide tailored exercises, bridging fall risk assessment and prevention (54,55). Both studies found this method to be feasible and found improvements in balance and mobility. These studies demonstrate feasible steps to incorporate mobile technology as a tool to measure risk factors and integrate into interventions.

P4 Model

The P4 model proposed by the World Congress on Falls and Postural Instability offers an innovative, person-centered approach to prevent falls. Personalization was defined as customizing diagnosis and management of fall risk, prediction as utilizing available information to determine an individual’s risk of falls, prevention as utilizing identified fall risks factors to develop individualized fall prevention plans, and participation as sharing data and strategies with older adults and involving them in treatment choices to improve adherence (19). Table 3 highlights whether fall risk applications have incorporated the P4 approach. Seven apps were considered personalized as they incorporated algorithms to measure multiple fall risk factors and/or exercises tailored to older adults (40,41,43,53–56). Five apps were predictive as they were developed to measure or predict fall risk (40,41,43,45,47). Seven apps incorporated prevention strategies, such as balance exercises, to reduce fall risk and demonstrated prevention (40,45,53–57). Last, 7 studies tested usability and designed their apps for older adult users, demonstrating participation (42,44–46,53,56,57). While 10 of the 12 apps addressed more than 1 “P,” none of the studies incorporated every component of the P4 model.

Table 3.

Fall Prevention Applications That Have Incorporated Components of the P4 Model

Study	Study Location	Personalization	Prediction	Prevention	Participation
Ambrens et al. (56)	Australia	X		X	X
Choi et al. (57)	United States			X	X
Greene et al. (40)	Ireland	X	X	X
Rabe et al. (41)	Germany	X	X
Hsieh et al. (42)	United States	X			X
Hsieh et al. (43).	United States		X
Mansson et al. (44)	Sweden				X
Hawley-Hague et al. (53)	United Kingdom	X		X	X
Netz et al. (54)	Israel	X		X
Papi et al. (55)	United Kingdom	X		X
Taheri-Kharameh et al. (45)	Iran		X	X	X
Zhong and Rau (46)	China				X
Rasche et al. (47)	Germany		X

Discussion

Because falls result from an interplay of multiple risk factors, mobile health technology offers an innovative solution to provide tailored fall risk screening and prevention. Indeed, mobile technology is well suited to provide the P4 model of fall prevention by offering a technological infrastructure for personalization, prediction, prevention, and participation. This narrative review highlights fall risk smartphone apps that were tested for validity, usability, and implementation. Ten studies utilized smartphone IMUs to measure static and dynamic balance in older adults, comparing balance performance to other technologies or clinical assessments (30–39). These studies suggest that smartphone IMUs are capable of quantifying static and dynamic balance tasks. Eight studies developed fall risk apps measuring varying fall risk constructs (40–47). Four studies tested the usability with older adult users (42,44–46), with only 1 study making iterative changes (42), and only 2 of the fall risk apps were implemented in community settings (40,43). Five apps delivered exercises to reduce fall risk in older adults, and most were feasible when integrated into a clinical trial (53–57). Mobile health apps are growing in popularity to measure mobility and fall risk. Future mobile health apps for fall risk prevention should build upon lessons learned from the current studies and designed with the unique needs of older adults.

Falls result from a complex array of physiological (balance, gait, lower limb function), psychosocial (fear of falling, balance confidence), cognitive (processing speed, attention), pharmacological, and environmental factors. The literature is rife with discussion on assessing these risk factors. This review discusses further evidence that smartphone IMUs can offer an objective approach to quantify balance and gait function in older adults. Importantly, there is some limited evidence that this approach is viable outside clinical settings. In contrast, research focusing on fall risk factors outside of balance and gait frequent rely on self-report, which may be subject to bias.

A significant portion of fall risk factors have yet to be quantified with mobile technology. For instance, cognitive testing and environmental constraints influence fall risk, and were not objectively measured in any of the fall risk apps developed. Previous studies have developed and validated cognitive testing with mobile technology, including measuring processing speed, executive function, and memory function for older adults (59,60). Another study utilized smartphone IMUs to quantify cognitive-motor inference (eg, walking while talking), a known risk factor for falls, in retired American football players (61). It is logical that these assessments could be integrated into fall-related applications. Future fall risk apps need to incorporate a multifactorial assessment of fall risk.

The majority of the apps developed focused on measuring fall risk factors for tailored screening. Screening is a critical step for older adults to understand their fall risk. However, only 2 of the apps included tailored prevention, and neither tested the effectiveness of mitigation strategies (53,56). Five apps integrated mobile technology into a falls prevention trial (53–58), but only 2 incorporated mobile technology as a fall risk assessment and prevention tool (40,45). While these pilot studies demonstrate feasibility, future work should focus on the “prevention” component of the P4 model and to develop individualized fall prevention plans. For instance, given the ability of mobile technology to track physical location, it is possible that applications could provide evidence-based fall mitigation strategies that are not only appropriate based on risk stratification but also locally available resources (Figure 1). Alternative strategies may be similar to those implemented by Ambrens et al. (56) and Netz et al. (54), which implements tailored, home-based exercises via mobile technology. Special attention also needs to be paid to adherence of any mitigation strategies. Such testing will determine whether fall prevention interventions with mobile technology are scalable and practical.

This review suggests that mobile technology is unique in that it can incorporate personalization, prediction, prevention, and participation to offer tailored falls prevention. Integrating each component of the P4 model is critical to effectively reduce fall risk at a global level. However, to date, none of the developed fall risk health apps have incorporated the entire P4 model. While most of the apps focus on prediction, few have employed robust algorithms to measure multiple risk factors. More studies are integrating user-centered approaches (ie, participation), but as previously discussed, few of the apps included methods of falls prevention. To improve these currently existing apps, further work is needed to include the entire P4 model. In addition, only 1 study, using the StandingTall app, examined falls as a result of a falls prevention intervention delivered through mobile technology. As future apps incorporate the entire P4 model, it is necessary to determine the effect of these apps on falls and fall-related injuries as primary outcome measures.

One limitation of the current fall risk apps is the lack of diversity in the older adult users. All of the studies included tested healthy, community-dwelling ambulatory older adults with limited diversity. It is likely that older adults with chronic disease conditions (ie, Parkinson’s disease, multiple sclerosis) and those with limited mobility may have unique fall risk factors and need specialized fall risk mobile health assessments (62,63). In addition, an advantage of mobile technology is its ability to reach diverse populations, including those who live in rural areas or non-English-speaking older adults. To prevent falls in these vulnerable populations, it is critical for future studies to design fall risk apps specific for these populations, their potentially unique risk factors, and develop strategies to reduce the impact of the digital divide.

In conclusion, this review identified fall risk health apps which appeared in peer-reviewed literature since 2017. Majority of studies quantified mobility with smartphone IMUs, suggesting that smartphones can objectively measure static and dynamic balance as a fall risk factor. Mobile health technology offers an innovative solution for implementing the P4 model of fall prevention in community settings. Moreover, future studies need to determine if mobile technology can offer tailored and scalable interventions. While there are numerous studies examining fall risk factors and clinical fall prevention trials, the literature on mobile technology and falls prevention in comparison is infinitesimal. Mobile technology offers substantial potential to prevent falls at the global level, and future work can bridge these gaps to provide personalized fall prevention approaches.

Funding

J.J.S. is funded by National Institutes of Health (R21AG073892); (R21AG064308-01); National Institute on Disability, Independent Living, and Rehabilitation Research (90DPHF0010; 90REGE0006-01-00); National Multiple Sclerosis Society (MB-1807-31633; RG-1701-26862). K.L.H. is funded by National Institute of Health (T32 AG033534).

Conflict of Interest

J.J.S. has ownership in Sosnoff Technologies, LLC, received speaking fees from BrainWeek and consulting fees from Xavor, Inc. The other authors declare no conflict.