The “Aachen fall prevention App” – a Smartphone application app for the self-assessment of elderly patients at risk for ground level falls

The incidence of falls in the older adult population is difficult to determine, but the consequences of falls are a major public health and economic issue [1‐4]. About 30% of community-dwelling people older than 65 years fall at least once a year [5]. Some evidence indicates that falls can be prevented [1]; therefore, the risk of falling needs to be identified and monitored, starting at an early age (50+ years). At present, patients have to consult their physician or a hospital for risk assessment, as the usual screening algorithms rely on trained individuals in a hospital setting [1]. Consequently, assessing the fall risk is not a low threshold service, so empowering patients with an independent pre-test of their fall risk by a home-based screening method would have considerable advantages. This method should be distinct from the more intensive assessment procedures currently used to identify potentially modifiable risk factors in multifactorial fall prevention programs [6]. A simple self-assessment approach would seem useful for monitoring individual fall risk in the first place and, in cases indicating an increased risk, a specialist could perform a clinical fall risk assessment [7]. Studies have shown that a simple balance test is a quite good indicator of a specific fall risk [8‐11]. Simple screening questions have also been identified to perform as well as more complex screening tests in predicting those who will fall [6]. The “Aachen Fall Prevention App” (AFPA) combines these ideas, based on the “Aachen Fall Prevention Scale” [7].

The objective fall risk significantly corresponded with the number of registered “yes” answers per participant for the ten standardized questions, F(1, 75) = 100.73, p <0,001, η² = 0.729. Participants with an objective fall risk answered more questions with “yes” during self-test than did the participants without an objective fall risk (positive criterion ≥ 5 “yes” responses; see Fig. 2). Furthermore, a significant positive relationship was revealed between the objective fall risk and failure in the ten-second free-standing test, F(2, 75) = 32.692, p <0.001, η² = 0.525. Participants with an objective fall risk failed the free-standing test more often (Fig. 2). Finally, the third one-way analyses of variance identified a significant positive relationship between the objective fall risk and the subjective fall risk rating, F(2, 75) = 6.033, p = 0.004, η² = 0.139. Participants with an objective fall risk rated their subjective fall risk as higher (Fig. 2).

The average number of times the participants performed the self-assessment was 1.7 (SD = 1.2) times, ranging from one time up to nine times per participant. No deviation of fall risk over repeated measurement was detected.

A significant relationship was identified between the objective fall risk and the self-assessment results obtained by the AFPA. Participants with a high objective fall risk also showed a high risk as measured by the AFPA. In the past, time-consuming clinical measurement sets showed shortcomings in discrimination between fallers and non-fallers based on a self-reported retrospective falls-status [14]. Therefore, this study was able to show the potential of mobile Health apps in the context of patient-driven fall risk assessment.

Clinical fall risk assessment usually starts at an age of 75 years [4, 6‐11]. The AFPA was downloaded voluntarily without specific recruitment by even younger participants, indicating a specific interest in this topic among older adults who use information and communication technology [15]. Hence, the app seems to be a suitable medium for offering a simple and low-threshold service for fall risk assessment in older adults. Nevertheless, the results of the first year of field study revealed a short duration of usage. Participants assessed their fall risk about two times, on average. Further work is needed to extend the capabilities of the study app to provide more than just self-assessment by offering suitable arrangements to support users’ self-paced prevention measures. Possible features might include guided instructions to strengthen power and balance. Further studies are also needed to investigate which incentives could be facilitated to increase adherence, as discussed in the context of health-related exergames [16]. Exergames run on mobile phones and the player is required to be physically active [16]. Medical and public health communities have discussed the potential of these games with regard to their influence on higher levels of sustainable physical activity to achieve health benefits [16]. Nevertheless, this study showed the potential of modern digital technology in the context of patient-driven fall risk assessment.

This study has certain limitations which should be mentioned. First the “Aachen Falls Prevention Scale” is still under evaluation and validation in the laboratory setting. Nevertheless, the results of this study indicate a suitable validity, as significant relationships were detected between the objective fall risk and the subjective risk measured by the AFPA.

Assessing patients’ fall risk and providing acceptable preventive measures remains an important research topic. We introduced the “Aachen Falls Prevention App” and presented initial results of the first year of an ongoing field study that recruits participants via the major app-stores of Apple and Google. The results show that this app is a useful supplement in healthcare, as it is a low-threshold service that supports patients in self-assessing their individual fall risk. Collection of more data over the next years will provide more insights for incorporating mHealth solutions into fall prevention and for getting patients initially involved in monitoring their individual fall risks.