Background
Falls and the often costly treatment of fall-related injuries are a burden for older people’s autonomy and independence as well as the healthcare system [
1]. Previous reviews and meta-analyses have shown that a range of successful strategies for fall prevention is available [
2,
3]. In order to prevent falls in older people, it is important to implement effective preventative programs on a broad scale.
It is well known from the literature that exercise interventions can reduce the risk of falling and rate of falls provided they are continued over a longer period of at least six months [
2,
4]. However, incorporating a new exercise regimen into daily life can be challenging for many older people due to poor exercise tolerance and enjoyment. Videogame technologies provide an opportunity to deliver exercise programs (exergaming) by offering increased convenience and greater level of engagement. Furthermore, several studies have suggested that their effectiveness towards improving key fall risk factors may be equivalent to traditional exercise programs of similar content and dosage [
5‐
7]. A recent systematic review provided preliminary evidence for the effectiveness of such innovative exercise modes on physical and cognitive factors associated with fall risk in older people [
8]. Furthermore, sensor-based virtual environments allow inclusion of dual-tasking to support task-specific training (e.g., cognitive tasks), continuous movement monitoring and real-time performance feedback which may improve long-term exercise adherence in older people [
9]. However, there is a lack of evidence on how fall prevention can be successfully implemented into the community, especially in older people who would like to exercise at home on their own [
4].
Despite promising evidence regarding the use of virtual reality and ambient-assistive technologies to deliver exercise programs in people’s homes, off-the-shelf exergame systems such as the Nintendo Wii or Microsoft Xbox are not sufficiently tailored to the specific circumstances and values of older people. Therefore, there is a need to design customized programs [
10]. We developed a new Information and Communication (ICT)-based system for fall risk assessment and fall prevention in older people living independently at home, called iStoppFalls (
www.istoppfalls.eu). The present study investigated the feasibility and effectiveness of the individually tailored ICT-based iStoppFalls exergame program delivered through the home television (TV) on fall risk factors. We hypothesized that this newly developed ICT-based system for fall prevention at home is feasible for older people in terms of exercise adherence, acceptability and safety. We also hypothesised that regular use of the iStoppFalls exercise program would lead to improved balance and strength outcomes (physiological fall risk) and quality of life. In addition, the exercise training may also be effective on the improvement of cognitive fall risk factors in older people.
Results
The flow of participants through the trial is illustrated in Fig.
1. One hundred fifty three older people were included in this study (
n = 78 intervention group,
n = 75 control group); 136 participants were reassessed after 16 weeks (
n = 28 exercise dropouts, of which
n = 11 agreed to be reassessed so that their data could be included in the ITT analyses). Baseline characteristics of the sample, showing no marked differences between groups, are displayed in Table
1. For the six month study period, a total of 24 falls were reported by monthly fall calendars, with eight falls being reported in the intervention group and 15 falls in the control group. No falls were related to using the iStoppFalls system. No other adverse events were reported.
Table 1
Participants’ characteristics at baseline presented as Mean ± Standard Deviation (SD) or number (percentage)
Age, years | 74.7 ± 6.3 | 74.7 ± 6.7 | 74.7 ± 6.0 |
Female, n (%) | 93 (61.2 %) | 43 (55.8 %) | 50 (66.7 %) |
Body mass index, kg/m2
| 26.3 ± 3.8 | 26.1 ± 3.8 | 26.5 ± 3.9 |
Education, years | 11 ± 5 | 12 ± 5 | 11 ± 4 |
Medication use (n) | 3.3 ± 2.8 | 3.5 ± 3.1 | 3.1 ± 2.5 |
One or more falls in the previous year, n (%) | 51 (34 %) | 25 (32.5 %) | 26 (35.6 %) |
Comorbidities, number | 3.0 ± 1.4 | 2.9 ± 1.3 | 3.1 ± 1.5 |
Heart problems, n (%) | 21 (14.1 %) | 12 (15.6 %) | 9 (12.5 %) |
High blood pressure, n (%) | 78 (53.1 %) | 35 (46.1 %) | 43 (60.6 %) |
Osteoporosis, n (%) | 35 (23.3 %) | 18 (23.4 %) | 17 (23.3 %) |
Lower back pain, n (%) | 61 (40.9 %) | 33 (43.4 %) | 28 (38.4 %) |
Hip pain, n (%) | 33 (22.4 %) | 16 (21.1 %) | 17 (23.9 %) |
Knee and/or leg pain, n (%) | 62 (41.6 %) | 29 (37.7 %) | 33 (45.8 %) |
Foot pain, n (%) | 36 (24.0 %) | 18 (23.4 %) | 18 (24.7 %) |
PAQ-50+, hours | 41.62 ± 21.20 | 40.55 ± 23.62 | 42.80 ± 18.70 |
IPEQ, hours | 26.45 ± 18.55 | 29.56 ± 16.91 | 23.13 ± 19.80 |
WHODAS, score | 15.79 ± 4.31 | 16.00 ± 4.52 | 15.57 ± 4.10 |
Computer ownership, n | 111 (72.6 %) | 58 (74.4 %) | 53 (70.7 %) |
Adherence and user experience
Over the 16-week training period, participants in the intervention group used the iStoppFalls system 42 times (median, interquartile range IQR = 57) at a game level of 2.1 (median, IQR = 3.9) for a total duration of 11.7 h (median, IQR = 22.0) including and 7.0 h (median, IQR = 12.8) excluding instructions, respectively. Eighteen participants exercised for a total of 24 h over the 16-week training period (1.5 h per week), of which two participants exceeded the recommended training dose of 48 h (3 h per week) and another four participants exercised for over 40 h (2.5 h per week). Of the remaining intervention group participants, 12 exercised for at least 16 h in total (1 h per week), 18 participants exercised for at least 8 h in total (30 min per week) and 30 participants exercised for less than 8 h in total. Balance exergames were performed 24 times (median, IQR = 30) at an average game level of 2.6 (median, IQR = 8.0) for a total duration of 4.0 h (median, IQR = 6.9) including instructions and 2.8 h (median, IQR = 4.1) excluding instructions. Strength exercises were performed 20 × (median, IQR = 31) at an average intensity level of 1.4 (median, IQR = 2.0) for a duration of 7.9 h (median, IQR = 13.4) including instructions and 4.4 h (median, IQR = 7.7) excluding instructions. Outcomes from the SMM were based on data analysis of 63 devices. Total SMM wearing time was 580 ± 459 h with a walking distance of 145 ± 45 m per hour and a generated peak power of 0.28 ± 0.07 arbitrary units across an average of 1.6 ± 1.4 detected sit-to-stand transfers per hour.
A mean score of 31 (standard deviation SD = 8) on the PACES suggested higher levels of enjoyment and a mean score of 62 (SD = 23) on the SUS suggested acceptable usability. Results for the DART questionnaire showed that the exergames were the most highly rated in terms of appeal, consistency, operation, speed, language and usability with a score of 4.4 points.
Effectiveness of the intervention
Intervention and control group comparison
Table
2 displays between-group differences after 16 weeks of intervention. Fall risk assessed by the PPA was significantly reduced in the intervention group compared with the control group (F
1,127 = 4.54,
p = 0.035). Hand reaction time worsened significantly in the control group compared to the intervention group (F
1,128 = 9.59,
p = 0.002). The reduction in time for 10 m walking while counting backwards by three was more pronounced in the intervention than the control group (F
1,127 = 4.32,
p = 0.040).
Table 2
Group comparison between baseline and post-assessment (Mean ± Standard Deviation, SD)
Primary outcome measures | | | | | |
Physiological Profile Assessment (score) | 0.62 ± 0.89 | 0.41 ± 0.95 | 0.55 ± 0.90 | 0.39 ± 0.80 | 0.035 |
European Quality of Life – 5 Dimensions (index) | 0.86 ± 0.11 | 0.86 ± 0.15 | 0.86 ± 0.13 | 0.87 ± 0.13 | 0.741 |
European Quality of Life – 5 Dimensions (VAS) | 79.2 ± 14.7 | 80.9 ± 13.7 | 81.7 ± 12.7 | 79.9 ± 14.6 | 0.244 |
Secondary outcome measures | | | | | |
General health measures |
WHODAS (score)b
| 16.0 ± 4.5 | 16.3 ± 4.8 | 15.6 ± 4.1 | 16.0 ± 4.5 | 0.494 |
Physical and sensorimotor measures |
Melbourne edge test (score) | 20.8 ± 1.6 | 21.0 ± 2.1 | 21.1 ± 1.8 | 21.2 ± 2.1 | 0.278 |
Proprioception (°)b
| 1.3 ± 1.0 | 1.2 ± 1.1 | 1.7 ± 1.3 | 1.3 ± 0.9 | 0.758 |
Knee extension strength (kg) | 23.0 ± 9.2 | 28.0 ± 11.9 | 22.7 ± 9.9 | 27.1 ± 14.0 | 0.545 |
Hand grip strength (kg) | 24.9 ± 9.4 | 23.8 ± 8.6 | 23.9 ± 9.0 | 24.0 ± 8.7 | 0.979 |
Hand reaction time (ms) | 245 ± 43 | 249 ± 47 | 239 ± 50 | 263 ± 45 | 0.002 |
Sway, area (mm2)b
| 841 ± 692 | 774 ± 1191 | 797 ± 596 | 629 ± 973 | 0.940 |
Coordinated stability, errorsb
| 11.3 ± 10.3 | 8.5 ± 9.0 | 12.2 ± 13.6 | 8.0 ± 9.0 | 0.909 |
Maximum balance range, anterior-posterior (mm) | 139 ± 41 | 145 ± 35 | 137 ± 48 | 152 ± 39 | 0.713 |
Timed up and go test (s) | 9.7 ± 2.8 | 9.5 ± 2.7 | 10.2 ± 3.1 | 9.2 ± 2.1 | 0.504 |
Short Physical Performance Battery (score) | 10.4 ± 1.7 | 11.1 ± 1.5 | 10.2 ± 1.6 | 10.9 ± 1.4 | 0.548 |
Sensor-based chair stand test (s)b
| 12.5 ± 4.2 | 10.8 ± 3.2 | 13.1 ± 3.7 | 12.1 ± 3.3 | 0.347 |
Sensor-based semi tandem stance (s) | 28.3 ± 6.0 | 28.9 ± 5.2 | 29.5 ± 3.0 | 29.2 ± 4.5 | 0.412 |
Sensor-based near tandem stance (s) | 28.1 ± 6.3 | 28.5 ± 5.7 | 28.3 ± 5.4 | 29.1 ± 4.5 | 0.876 |
Sensor-based full tandem stance (s) | 24.4 ± 9.9 | 24.4 ± 9.5 | 23.3 ± 10.2 | 25.0 ± 9.2 | 0.727 |
Sensor-based hand reaction time (ms)b
| 698 ± 142 | 619 ± 131 | 705 ± 391 | 649 ± 162 | 0.259 |
Sensor-based stepping reaction time (ms)b
| 794 ± 199 | 732 ± 209 | 801 ± 142 | 743 ± 121 | 0.055 |
10 m walking time, single-tasking (s)b
| 8.9 ± 2.0 | 8.7 ± 1.8 | 8.9 ± 2.3 | 8.6 ± 1.8 | 0.323 |
10 m walking time, dual-tasking (s)b
| 12.0 ± 3.9 | 11.4 ± 3.8 | 12.0 ± 4.5 | 11.7 ± 4.0 | 0.040 |
Cognitive measures |
Trail Making Test, part A (s)b
| 42.8 ± 17.5 | 40.4 ± 16.5 | 44.2 ± 18.1 | 39.5 ± 14.1 | 0.736 |
Trail Making Test, part B (s)b
| 114.5 ± 55.8 | 100.6 ± 51.1 | 116.2 ± 52.2 | 96.3 ± 47.9 | 0.474 |
Digit symbol coding test, correct (n) | 49.4 ± 15.6 | 51.4 ± 15.7 | 44.5 ± 13.0 | 48.6 ± 11.9 | 0.444 |
Digit span backward test, score (n) | 6.0 ± 2.3 | 6.1 ± 2.1 | 5.9 ± 2.3 | 5.7 ± 2.5 | 0.178 |
Attention Network Test, reaction time (ms)b
| 808 ± 134 | 789 ± 130 | 810 ± 116 | 745 ± 97 | 0.121 |
Attention Network Test, alert (ms) | 29.5 ± 34.3 | 37.3 ± 30.0 | 35.4 ± 33.8 | 39.1 ± 32.2 | 0.262 |
Attention Network Test, orient (ms) | 44.4 ± 38.2 | 44.0 ± 40.1 | 51.5 ± 42.1 | 59.0 ± 40.6 | 0.561 |
Attention Network Test, conflict (ms)b
| 134.3 ± 74.7 | 117.3 ± 53.3 | 127.1 ± 60.3 | 126.7 ± 63.5 | 0.329 |
Victoria Stroop Test, intrusions (n)b
| 3.0 ± 3.4 | 2.3 ± 3.4 | 3.2 ± 4.0 | 2.6 ± 4.3 | 0.802 |
Victoria Stroop Test, efficacy (colour words/words)b
| 1.69 ± 0.73 | 1.52 ± 0.67 | 1.63 ± 0.74 | 1.48 ± 0.60 | 0.412 |
Psychological measures |
Iconographical – Falls Efficacy Scale (score) | 16.2 ± 5.3 | 12.6 ± 8.5 | 15.4 ± 4.4 | 11.0 ± 7.5 | 0.593 |
Patient Health Questionnaire – 9 (score)b
| 3.06 ± 3.14 | 2.48 ± 2.92 | 2.99 ± 2.97 | 2.58 ± 2.38 | 0.340 |
Pre-planned subgroup analyses
Tables
3 and
4 shows the results of the pre-planned subgroup analyses for exercise dose. There was a significant three-way interaction for fall risk assessed by the PPA between the high-adherence (>90 min exercise per week), low-adherence (<90 min exercise per week) and control group (F
2,125 = 3.12,
p = 0.044). Two-way analyses comparing the high-adherence group with the control group revealed a significant larger effect in favour of the high-adherence group for fall risk (F
1,77 = 4.82,
p = 0.031), postural sway (F
1,75 = 4.13,
p = 0.046), executive function (F
1,71 = 4.21,
p = 0.044) as well as sensor-based full tandem stance time (F
1,73 = 4.35,
p = 0.040) and stepping reaction time (F
1,75 = 4.40,
p = 0.041). There was also a trend for an improvement in quality of life (F
1,73 = 3.91,
p = 0.052, borderline) when the high-adherence group was compared to the control group.
Table 3
Participant characteristics at baseline (mean ± Standard Deviation, SD) comparing subgroups of low and high adherence
Age, years | 73.1 ± 6.7 | 74.9 ± 6.3 | 0.556 |
Female, n (%) | 12 (66.7 %) | 26 (49.1 %) | 0.112 |
Body mass index, kg/m2 | 24.8 ± 3.5 | 26.5 ± 4.0 | 0.223 |
Education, years | 12 ± 5 | 11 ± 5 | 0.906 |
Medication use (n) | 2.6 ± 2.4 | 3.6 ± 3.1 | 0.368 |
One or more falls in the previous 12 months, n (%) | 6 (33.3 %) | 16 (30.8 %) | 0.852 |
Comorbidities, number | 2.8 ± 1.2 | 2.9 ± 1.4 | 0.747 |
WHODAS, score | 14.9 ± 3.0 | 15.7 ± 4.1 | 0.746 |
Table 4
Subgroup analyses for exercise adherence Group comparison between baseline and post-assessment (Mean ± Standard Deviation)
Primary outcome measures | | | | |
p
|
p
|
p
|
Physiological Profile Assessment, score | 0.84 ± .85 | 0.39 ± .77 | 0.55 ± 0.93 | 0.34 ± .91 | 0.430 | 0.044 | 0.031 |
European Quality of Life – 5 Dimensions, VAS | 70.7 ± 13.9 | 80.3 ± 13.17 | 80.6 ± 14.6 | 80.8 ± 14.18 | 0.057 | 0.099 | 0.052 |
Selected secondary outcome measures | | | | | | | |
Sway area, mm2
| 1093 ± 778 | 554 ± 487 | 754.9 ± 662 | 718 ± 1048 | 0.150 | 0.057 | 0.046 |
Sensor-based full tandem stance, s | 25.0 ± 10.0 | 26.5 ± 8.0 | 25.2 ± 9.5 | 23.1 ± 10.0 | 0.568 | 0.092 | 0.040 |
Knee extension strength, kg | 20.1 ± 6.4 | 27.8 ± 15.8 | 24.5 ± 9.8 | 28.0 ± 10.6 | 0.277 | 0.425 | 0.291 |
Sensor-based chair stand test, s | 11.1 ± 2.9 | 10.4 ± 3.0 | 12.6 ± 4.4 | 10.8 ± 10.8 | 0.133 | 0.053 | 0.983 |
Sensor-based stepping reaction time, ms | 756 ± 161 | 657 ± 107 | 788 ± 196 | 731 ± 140 | 0.685 | 0.112 | 0.041 |
Trail Making Test part B, s | 108 ± 52 | 103 ± 54 | 117 ± 58 | 98 ± 50 | 0.811 | 0.860 | 0.591 |
Hand reaction time, ms | 260 ± 36 | 276 ± 49 | 242 ± 41 | 238 ± 42 | 0.239 | 0.004 | 0.505 |
Attention Network Test - conflict, ms | 152 ± 63 | 114 ± 57 | 127 ± 82 | 117 ± 53 | 0.394 | 0.088 | 0.044 |
Patient Health Questionnaire – 9, score | 3.4 ± 3.0 | 2.8 ± 2.8 | 3.0 ± 3.4 | 2.4 ± 3.0 | 0.945 | 0.434 | 0.213 |
Iconographical – Falls Efficacy Scale, score | 13.8 ± 4.3 | 10.2 ± 7.0 | 16.3 ± 5.3 | 13.3 ± 8.9 | 0.173 | 0.775 | 0.854 |
Additional subgroup analyses based on baseline fall risk (cut-point = 0.540, median split for PPA score at baseline) showed a significant three-way interaction between high fall risk (PPA >0.540), low fall risk (PPA <0.540) and the control group (F2,128 = 6.90, p = 0.001). Two-way, post hoc analyses revealed that the high-fall risk group had a significant larger decrease in PPA score compared to the low-fall risk (p = 0.003) and control group (p = 0.001). Total exercise duration was higher in the high-risk group (15.8 h, SD 22.9) than in the low-risk group (9.4 h, SD 14.7); however, this difference was not statistically significant.
Discussion
Our study findings suggest the ICT-based iStoppFalls exercise program is feasible for use in the homes of older community-dwelling people with initial instructor support. There were no adverse events or falls reported related to undertaking the intervention, suggesting that the unsupervised, home-based exercise program using the iStoppFalls system is feasible in terms of safety for older people. Overall, the exercise program was enjoyed by the participants and its usability was acceptable, however more work is needed to optimize adherence to the program. In line with our hypotheses, there was a larger reduction in our primary outcome of physiological fall risk in the intervention group compared to the control group, and also a small improvement in dual-tasking ability while walking. The intention-to-treat (ITT) analyses did not show a significant effect on the second primary outcome quality of life. Participants who exercised for more than 90 min per week (high-adherers) reduced their physiological fall risk significantly more than the control group. In addition, the sway area, stepping reaction time and ANT conflict measurements decreased, full tandem stance time increased and there was a trend for improved quality of life in the high-adherers.
The iStoppFalls exercise program reduced physiological fall risk in the study sample. The significant reduction in overall physiological fall risk scores (33.9 % ITT, 53.6 % high-adherers) was due to small cumulative improvements in some of the PPA sub-components (i.e., visual contrast sensitivity, quadriceps strength and postural sway). This pattern of small, cumulative benefits across physiological domains following an exercise intervention has also been reported in a previous study targeting older people who had recently been discharged from hospital [
40]. However, while the overall reduction in PPA score seems clinically meaningful, it needs to be demonstrated in further studies that the program is equally effective in improving the core outcome measures of balance and muscle strength in a larger population of older people to warrant recommendation as a fall prevention intervention. Furthermore, the significant difference in reaction time is largely due to an unexpected worsening in the control group, instead of an improvement in the intervention group. On the other hand, our subgroup analyses showed that participants with higher physiological fall risk benefited most from the intervention, suggesting that the iStoppFalls system might be particularly useful in people at increased risk of falling.
Dual-tasking ability has been consistently associated with falls, and improving this ability through task-specific training might benefit people during daily life activities and possibly prevent falls. Similar to other studies including interactive exergaming [
39,
41], our system was able to improve the motor component of dual–tasking ability as participants in the intervention group improved their 10-m walk times while counting backwards. In higher exergame levels, dual task components (e.g., memorising an object and identifying it from a selection at a later stage) were added to provide additional cognitive stimuli. However, it should be noted that the improvement was relatively small, but statistically significant. In relation to more specific cognitive measures, no intervention effects were evident. There was an indication that executive function could be improved in participants who exercised 90 min or more per week. One possible explanation could be that the dual task training as part of the higher exergame levels was not challenging enough for the included participants to improve other aspects of cognitive function.
Previous studies have found that increasing physical activity is effective in enhancing quality of life of older people over relatively short periods of time [
42,
43]. In the current study, there was no significant improvement in quality of life following the intervention. However, there was a trend indicating improved quality of life in the intervention group participants who exercised for 90 min or more. Therefore, it will be important for older people to incorporate exercise as delivered through the iStoppFalls system into their lifestyle at higher doses in order to improve their quality of life [
44].
Older people often use stepping as a strategy to regain postural stability after balance is lost in order to prevent a fall [
45]. Impairment in stepping reaction time has been reported previously to be an independent predictor of falls [
46]. In the present study, high-adherers (>90 min exercise per week) significantly improved their stepping reaction time compared to the control group. The ability to step faster may help older individuals to withstand unexpected balance perturbations and therefore reduce the risk of falling.
Strengths of the iStoppFalls system include the automatically-generated exercise adherence data for frequency, duration and intensity/game level, automatic reminders to exercise, and the provision of in-home instructions, immediate performance feedback and scoring information. Additionally, it has been shown that the fall risk assessment function of the iStoppFalls system was feasible for regular self-assessment of fall risk at home and that it was able to discriminate well between older fallers and non-fallers [
47]. Another advantage of the Kinect-based system is that no further physical equipment (e.g., balance board) is needed to perform the exercises compared to other exergame systems.
The study had several limitations. With respect to the dose–response relationship, the low adherence rate may explain the limited improvement of secondary outcome measures. The cut-off point based on weekly exercise of 90 min duration was well below the recommended duration of 180 min per week. In this context it has to be considered that we were able to measure the “real” exercise time (i.e., core exercise excluding breaks or instructions – usually included in most intervention studies) very accurately with the iStoppFalls system. However, the ambitious goal which was based on recommendations for fall prevention exercise may have discouraged some participants [
15]. A meta-analysis on adherence to home exercise programs to prevent falls by Simek et al. [
48] reported 21 % fully adherent participants (95 % confidence interval: 15–29 %, range: 0-68 %). Our study reported that 23 % of all intervention participants adhered to a training dose of 90 min per week (which is similar to the most commonly prescribed dose across home-based exercise trials [
48]). In this context, it is noteworthy that the power calculation was based on participants exercising more frequently, and the low exercise stimulus may explain the lack of statistically significant effects on a wide range of secondary outcomes. On the other hand, based on the results from our pre-planned subgroup analyses, we can suggest that half the recommended training dose was sufficient to induce some positive training effects.
A recent systematic review concluded that virtual reality gaming systems can be used by older people at home, but feasibility was generally poorly described [
49]. The authors also reported that standby technology assistance, close monitoring and home visits were required in many studies. In our study, at least one additional house visit per participant in combination with technical support by telephone and PC remote had to be applied. It is possible that these technical difficulties may have prevented regular exercising (especially in the first month of the trial), which could further explain the low adherence rates. Therefore, more research and development work is needed before this approach can be recommended as an unsupervised training program.
Conclusions
In conclusion, the 16-week iStoppFalls exercise program reduced physiological fall risk as hypothesized and additional subgroup analyses revealed that intervention participants with better adherence also improved in postural sway, stepping reaction and executive function. Our study findings suggest that iStoppFalls is feasible for use in the homes of older community-dwelling people with initial instructor support. The relatively low adherence to the recommended dose, which can be largely explained by the pioneering use of new technology, indicates that further research and development is required to improve the adherence and thus the effectiveness of exergame systems for fall prevention.
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients before being included in the study.
Acknowledgements
The iStoppFalls project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no [287361]. The Australian arm was funded by an Australian National Health and Medical Research Council (NHMRC) EU collaboration grant (#1038210). The content of the manuscript does not represent the opinion of the European Community or NHMRC. The funding sources had no influence in any aspects of this study.
Yves J. Gschwind was financially supported by a research grant from the Margarete and Walter Lichtenstein Foundation, Basel, Switzerland. Stephen R. Lord was supported by NHMRC as a Senior Principal Research Fellow. Kim Delbaere was supported by a NHMRC as a Career Development Fellow. The other authors were supported by the European Community Grant Agreement 287361.
We would like to thank all the technical engineers, software developers and research staff who contributed to the iStoppFalls project as well as the participants who took part in the study.
Competing interests
The PPA (NeuRA FallScreen) and the not-for-profit iPad applications of the Incidental and Planned Exercise Questionnaire (IPEQ) and Iconographical Falls Efficacy Scale (iconFES) are commercially available through Neuroscience Research Australia. The authors declare that they have no further competing interests. Funding sources did not influence the design or implementation of the study as declared in the acknowledgements.
Authors’ contributions
Funding for the study was obtained by KD and RW. All authors were involved in the design and conduct of the trial. All authors contributed to writing the manuscript. YG and KD drafted the manuscript. All authors critically revised and approved the final manuscript. The authors declare that the submitted paper, the data, and the results have not been published anywhere before.