Andreas Ejupi

Bio: Andreas Ejupi is an academic researcher from Simon Fraser University. The author has contributed to research in topics: Poison control & Fall prevention. The author has an hindex of 14, co-authored 18 publications receiving 565 citations. Previous affiliations of Andreas Ejupi include Vienna University of Technology & University of New South Wales.

ICT-based system to predict and prevent falls (iStoppFalls): results from an international multicenter randomized controlled trial

Abstract: Falls and fall-related injuries are a serious public health issue. Exercise programs can effectively reduce fall risk in older people. The iStoppFalls project developed an Information and Communication Technology-based system to deliver an unsupervised exercise program in older people’s homes. The primary aims of the iStoppFalls randomized controlled trial were to assess the feasibility (exercise adherence, acceptability and safety) of the intervention program and its effectiveness on common fall risk factors. A total of 153 community-dwelling people aged 65+ years took part in this international, multicentre, randomized controlled trial. Intervention group participants conducted the exercise program for 16 weeks, with a recommended duration of 120 min/week for balance exergames and 60 min/week for strength exercises. All intervention and control participants received educational material including advice on a healthy lifestyle and fall prevention. Assessments included physical and cognitive tests, and questionnaires for health, fear of falling, number of falls, quality of life and psychosocial outcomes. The median total exercise duration was 11.7 h (IQR = 22.0) over the 16-week intervention period. There were no adverse events. Physiological fall risk (Physiological Profile Assessment, PPA) reduced significantly more in the intervention group compared to the control group (F1,127 = 4.54, p = 0.035). There was a significant three-way interaction for fall risk assessed by the PPA between the high-adherence (>90 min/week; n = 18, 25.4 %), low-adherence (<90 min/week; n = 53, 74.6 %) and control group (F2,125 = 3.12, n = 75, p = 0.044). Post hoc analysis revealed a significantly larger effect in favour of the high-adherence group compared to the control group for fall risk (p = 0.031), postural sway (p = 0.046), stepping reaction time (p = 0.041), executive functioning (p = 0.044), and quality of life (p for trend = 0.052). The iStoppFalls exercise program reduced physiological fall risk in the study sample. Additional subgroup analyses revealed that intervention participants with better adherence also improved in postural sway, stepping reaction, and executive function. Australian New Zealand Clinical Trials Registry Trial ID: ACTRN12614000096651 International Standard Randomised Controlled Trial Number: ISRCTN15932647

Kinect-Based Five-Times-Sit-to-Stand Test for Clinical and In-Home Assessment of Fall Risk in Older People.

Abstract: Background Accidental falls remain an important problem in older people. The five-times-sit-to-stand (5STS) test is commonly used as a functional test to assess fall risk. Recent advances in sensor technologies hold great promise for more objective and accurate assessments. Objective The aims of this study were: (1) to examine the feasibility of a low-cost and portable Kinect-based 5STS test to discriminate between fallers and nonfallers and (2) to investigate whether this test can be used for supervised clinical, supervised and unsupervised in-home fall risk assessments. Methods A total of 94 community-dwelling older adults were assessed by the Kinect-based 5STS test in the laboratory and 20 participants were tested in their own homes. An algorithm was developed to automatically calculate timing- and speed-related measurements from the Kinect-based sensor data to discriminate between fallers and nonfallers. The associations of these measurements with standard clinical fall risk tests and the results of supervised and unsupervised in-home assessments were examined. Results Fallers were significantly slower than nonfallers on Kinect-based measures. The mean velocity of the sit-to-stand transitions discriminated well between the fallers and nonfallers based on 12-month retrospective fall data. The Kinect-based measures collected in the laboratory correlated strongly with those collected in the supervised (r = 0.704-0.832) and unsupervised (r = 0.775-0.931) in-home assessments. Conclusion In summary, we found that the Kinect-based 5STS test discriminated well between the fallers and nonfallers and was feasible to administer in clinical and supervised in-home settings. This test may be useful in clinical settings for identifying high-risk fallers for further intervention or for regular in-home assessments in the future.

ICT-based system to predict and prevent falls (iStoppFalls): study protocol for an international multicenter randomized controlled trial

Abstract: Falls are very common, especially in adults aged 65 years and older. Within the current international European Commission’s Seventh Framework Program (FP7) project ‘iStoppFalls’ an Information and Communication Technology (ICT) based system has been developed to regularly assess a person’s risk of falling in their own home and to deliver an individual and tailored home-based exercise and education program for fall prevention. The primary aims of iStoppFalls are to assess the feasibility and acceptability of the intervention program, and its effectiveness to improve balance, muscle strength and quality of life in older people. This international, multicenter study is designed as a single-blinded, two-group randomized controlled trial. A total of 160 community-dwelling older people aged 65 years and older will be recruited in Germany (n = 60), Spain (n = 40), and Australia (n = 60) between November 2013 and May 2014. Participants in the intervention group will conduct a 16-week exercise program using the iStoppFalls system through their television set at home. Participants are encouraged to exercise for a total duration of 180 minutes per week. The training program consists of a variety of balance and strength exercises in the form of video games using exergame technology. Educational material about a healthy lifestyle will be provided to each participant. Final reassessments will be conducted after 16 weeks. The assessments include physical and cognitive tests as well as questionnaires assessing health, fear of falling, quality of life and psychosocial determinants. Falls will be followed up for six months by monthly falls calendars. We hypothesize that the regular use of this newly developed ICT-based system for fall prevention at home is feasible for older people. By using the iStoppFalls sensor-based exercise program, older people are expected to improve in balance and strength outcomes. In addition, the exercise training may have a positive impact on quality of life by reducing the risk of falls. Taken together with expected cognitive improvements, the individual approach of the iStoppFalls program may provide an effective model for fall prevention in older people who prefer to exercise at home. Australian New Zealand Clinical Trials Registry Trial ID: ACTRN12614000096651 . International Standard Randomised Controlled Trial Number: ISRCTN15932647 .

Comparison between clinical gait and daily-life gait assessments of fall risk in older people

Abstract: Aim Falls are a leading cause of disability in older people. Here we investigate if daily-life gait assessments are better than clinical gait assessments at discriminating between older people with and without a history of falls. Methods A total of 96 independent-living participants (age 75.5 ± 7.8) underwent sensorimotor, psychological and cognitive assessments, and the Timed Up and Go and 10-m walk tests. Participants wore a small pendant sensor device for a week in their home environment, from which the new remote assessments of daily-life gait were determined. Results During daily-life, fallers had significantly lower gait quality (lower gait endurance, higher within-walk variability and lower between-walk adaptability), but not reduced gait quantity (total steps) or gait intensity (mean cadence). In the clinic, fallers had slower Timed Up and Go, but not 10-m walk test times. After adjusting for demographics, only the daily-life assessments of gait endurance and within-walk variability remained significant. Reduced daily-life gait assessments were significantly correlated with older age, higher body mass index, multiple medications, disability, more concern about falling, poor executive function and higher physiological fall risk. Conclusions The new daily-life gait assessments were better than the clinical gait assessments at identifying fall risk in our sample of independent living older people. However, further research is required to validate these findings in other populations or those living in residential aged care. Daily-life gait was not only associated with demographics and physiological capacity, but also general health, executive function and the ability to undertake a variety of activities of daily living without excessive concern about falling. Geriatr Gerontol Int 2017; 17: 2274–2282.

Wavelet-Based Sit-To-Stand Detection and Assessment of Fall Risk in Older People Using a Wearable Pendant Device

Abstract: Goal: Wearable devices provide new ways to identify people who are at risk of falls and track long-term changes of mobility in daily life of older people. The aim of this study was to develop a wavelet-based algorithm to detect and assess quality of sit-to-stand movements with a wearable pendant device. Methods: The algorithm used wavelet transformations of the accelerometer and barometric air pressure sensor data. Detection accuracy was tested in 25 older people performing 30 min of typical daily activities. The ability to differentiate between people who are at risk of falls from people who are not at risk was investigated by assessing group differences of sensor-based sit-to-stand measurements in 34 fallers and 60 nonfallers (based on 12-month fall history) performing sit-to-stand movements as part of a laboratory study. Results: Sit-to-stand movements were detected with 93.1% sensitivity and a false positive rate of 2.9% during activities of daily living. In the laboratory study, fallers had significantly lower maximum acceleration, velocity, and power during the sit-to-stand movement compared to nonfallers. Conclusion: The new wavelet-based algorithm accurately detected sit-to-stand movements in older people and differed significantly between older fallers and nonfallers. Significance: Accurate detection and quantification of sit-to-stand movements may provide objective assessment and monitoring of fall risk during daily life in older people.

Exercise for Preventing Falls in Older People Living in the Community

Abstract: Background At least one-third of community-dwelling people over 65 years of age fall each year. Exercises that target balance, gait and muscle strength have been found to prevent falls in these people. An up-to-date synthesis of the evidence is important given the major long-term consequences associated with falls and fall-related injuries OBJECTIVES: To assess the effects (benefits and harms) of exercise interventions for preventing falls in older people living in the community. Search methods We searched CENTRAL, MEDLINE, Embase, three other databases and two trial registers up to 2 May 2018, together with reference checking and contact with study authors to identify additional studies. Selection criteria We included randomised controlled trials (RCTs) evaluating the effects of any form of exercise as a single intervention on falls in people aged 60+ years living in the community. We excluded trials focused on particular conditions, such as stroke. Data collection and analysis We used standard methodological procedures expected by Cochrane. Our primary outcome was rate of falls. Main results We included 108 RCTs with 23,407 participants living in the community in 25 countries. There were nine cluster-RCTs. On average, participants were 76 years old and 77% were women. Most trials had unclear or high risk of bias for one or more items. Results from four trials focusing on people who had been recently discharged from hospital and from comparisons of different exercises are not described here.Exercise (all types) versus control Eighty-one trials (19,684 participants) compared exercise (all types) with control intervention (one not thought to reduce falls). Exercise reduces the rate of falls by 23% (rate ratio (RaR) 0.77, 95% confidence interval (CI) 0.71 to 0.83; 12,981 participants, 59 studies; high-certainty evidence). Based on an illustrative risk of 850 falls in 1000 people followed over one year (data based on control group risk data from the 59 studies), this equates to 195 (95% CI 144 to 246) fewer falls in the exercise group. Exercise also reduces the number of people experiencing one or more falls by 15% (risk ratio (RR) 0.85, 95% CI 0.81 to 0.89; 13,518 participants, 63 studies; high-certainty evidence). Based on an illustrative risk of 480 fallers in 1000 people followed over one year (data based on control group risk data from the 63 studies), this equates to 72 (95% CI 52 to 91) fewer fallers in the exercise group. Subgroup analyses showed no evidence of a difference in effect on both falls outcomes according to whether trials selected participants at increased risk of falling or not.The findings for other outcomes are less certain, reflecting in part the relatively low number of studies and participants. Exercise may reduce the number of people experiencing one or more fall-related fractures (RR 0.73, 95% CI 0.56 to 0.95; 4047 participants, 10 studies; low-certainty evidence) and the number of people experiencing one or more falls requiring medical attention (RR 0.61, 95% CI 0.47 to 0.79; 1019 participants, 5 studies; low-certainty evidence). The effect of exercise on the number of people who experience one or more falls requiring hospital admission is unclear (RR 0.78, 95% CI 0.51 to 1.18; 1705 participants, 2 studies, very low-certainty evidence). Exercise may make little important difference to health-related quality of life: conversion of the pooled result (standardised mean difference (SMD) -0.03, 95% CI -0.10 to 0.04; 3172 participants, 15 studies; low-certainty evidence) to the EQ-5D and SF-36 scores showed the respective 95% CIs were much smaller than minimally important differences for both scales.Adverse events were reported to some degree in 27 trials (6019 participants) but were monitored closely in both exercise and control groups in only one trial. Fourteen trials reported no adverse events. Aside from two serious adverse events (one pelvic stress fracture and one inguinal hernia surgery) reported in one trial, the remainder were non-serious adverse events, primarily of a musculoskeletal nature. There was a median of three events (range 1 to 26) in the exercise groups.Different exercise types versus controlDifferent forms of exercise had different impacts on falls (test for subgroup differences, rate of falls: P = 0.004, I² = 71%). Compared with control, balance and functional exercises reduce the rate of falls by 24% (RaR 0.76, 95% CI 0.70 to 0.81; 7920 participants, 39 studies; high-certainty evidence) and the number of people experiencing one or more falls by 13% (RR 0.87, 95% CI 0.82 to 0.91; 8288 participants, 37 studies; high-certainty evidence). Multiple types of exercise (most commonly balance and functional exercises plus resistance exercises) probably reduce the rate of falls by 34% (RaR 0.66, 95% CI 0.50 to 0.88; 1374 participants, 11 studies; moderate-certainty evidence) and the number of people experiencing one or more falls by 22% (RR 0.78, 95% CI 0.64 to 0.96; 1623 participants, 17 studies; moderate-certainty evidence). Tai Chi may reduce the rate of falls by 19% (RaR 0.81, 95% CI 0.67 to 0.99; 2655 participants, 7 studies; low-certainty evidence) as well as reducing the number of people who experience falls by 20% (RR 0.80, 95% CI 0.70 to 0.91; 2677 participants, 8 studies; high-certainty evidence). We are uncertain of the effects of programmes that are primarily resistance training, or dance or walking programmes on the rate of falls and the number of people who experience falls. No trials compared flexibility or endurance exercise versus control. Authors' conclusions Exercise programmes reduce the rate of falls and the number of people experiencing falls in older people living in the community (high-certainty evidence). The effects of such exercise programmes are uncertain for other non-falls outcomes. Where reported, adverse events were predominantly non-serious.Exercise programmes that reduce falls primarily involve balance and functional exercises, while programmes that probably reduce falls include multiple exercise categories (typically balance and functional exercises plus resistance exercises). Tai Chi may also prevent falls but we are uncertain of the effect of resistance exercise (without balance and functional exercises), dance, or walking on the rate of falls.

Comparisons of Interventions for Preventing Falls in Older Adults: A Systematic Review and Meta-analysis.

Abstract: Importance Falls result in substantial burden for patients and health care systems, and given the aging of the population worldwide, the incidence of falls continues to rise. Objective To assess the potential effectiveness of interventions for preventing falls. Data Sources MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Ageline databases from inception until April 2017. Reference lists of included studies were scanned. Study Selection Randomized clinical trials (RCTs) of fall-prevention interventions for participants aged 65 years and older. Data Extraction and Synthesis Pairs of reviewers independently screened the studies, abstracted data, and appraised risk of bias. Pairwise meta-analysis and network meta-analysis were conducted. Main Outcomes and Measures Injurious falls and fall-related hospitalizations. Results A total of 283 RCTs (159 910 participants; mean age, 78.1 years; 74% women) were included after screening of 10 650 titles and abstracts and 1210 full-text articles. Network meta-analysis (including 54 RCTs, 41 596 participants, 39 interventions plus usual care) suggested that the following interventions, when compared with usual care, were associated with reductions in injurious falls: exercise (odds ratio [OR], 0.51 [95% CI, 0.33 to 0.79]; absolute risk difference [ARD], −0.67 [95% CI, −1.10 to −0.24]); combined exercise and vision assessment and treatment (OR, 0.17 [95% CI, 0.07 to 0.38]; ARD, −1.79 [95% CI, −2.63 to −0.96]); combined exercise, vision assessment and treatment, and environmental assessment and modification (OR, 0.30 [95% CI, 0.13 to 0.70]; ARD, −1.19 [95% CI, −2.04 to −0.35]); and combined clinic-level quality improvement strategies (eg, case management), multifactorial assessment and treatment (eg, comprehensive geriatric assessment), calcium supplementation, and vitamin D supplementation (OR, 0.12 [95% CI, 0.03 to 0.55]; ARD, −2.08 [95% CI, −3.56 to −0.60]). Pairwise meta-analyses for fall-related hospitalizations (2 RCTs; 516 participants) showed no significant association between combined clinic- and patient-level quality improvement strategies and multifactorial assessment and treatment relative to usual care (OR, 0.78 [95% CI, 0.33 to 1.81]). Conclusions and Relevance Exercise alone and various combinations of interventions were associated with lower risk of injurious falls compared with usual care. Choice of fall-prevention intervention may depend on patient and caregiver values and preferences.

Smart Home Based on WiFi Sensing: A Survey

Abstract: Conventional sensing methodologies for smart home are known to be labor-intensive and complicated for practical deployment. Thus, researchers are resorting to alternative sensing mechanisms. Wi-Fi is one of the key technologies that enable connectivity for smart home services. Apart from its primary use for communication, Wi-Fi signal has now been widely leveraged for various sensing tasks, such as gesture recognition and fall detection, due to its sensitivity to environmental dynamics. Building smart home based on Wi-Fi sensing is cost-effective, non-invasive, and enjoys convenient deployment. In this paper, we survey the recent advances in the smart home systems based on the Wi-Fi sensing, mainly in such areas as health monitoring, gesture recognition, contextual information acquisition, and authentication.