Ambulatory measurements of trunk accelerations can provide valuable information on the amount and quality of daily-life activities and contribute to the identification of individuals at risk of falls. We compared associations between retrospective and prospective falls with potential risk factors as measured by daily-life accelerometry. In addition, we investigated predictive value of these parameters for 6-month prospective falls. One week of trunk accelerometry (DynaPort MoveMonitor) was obtained in 169 older adults (mean age 75). The amount of daily activity and quality of gait were determined and validated questionnaires on fall-risk factors, grip strength, and trail making test were obtained. Six-month fall incidence was obtained retrospectively by recall and prospectively by fall diaries and monthly telephone contact. Among all participants, 35.5% had a history of ≥1 falls and 34.9% experienced ≥1 falls during 6-month follow-up. Logistic regressions showed that questionnaires, grip strength, and trail making test, as well as the amount and quality of gait, were significantly associated with falls. Significant associations differed between retrospective and prospective analyses although odds ratios indicated similar patterns. Predictive ability based on questionnaires, grip strength, and trail making test (area under the curve .68) improved substantially by accelerometry-derived parameters of the amount of gait (number of strides), gait quality (complexity, intensity, and smoothness), and their interactions (area under the curve .82). Daily-life accelerometry contributes substantially to the identification of individuals at risk of falls, and can predict falls in 6 months with good accuracy.

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data.

Background: Ageing is associated with physical and cognitive decline, affecting independence and quality of life in older people. Recent studies show that in particular executive functions are important for daily-life function and mobility. This systematic review investigated the effectiveness of cognitive-motor training including exergaming on executive function (EF, set-shifting, working memory, inhibitory control) in healthy older people. Methods: An electronic database search for randomised controlled trials (RCT), controlled clinical trials (CCT) and parallel group trials was performed using MEDLINE, EMBASE, and PsychINFO following PRISMA guidelines. Inclusion criteria were: (1) community-dwelling participants > 60 years without a medical condition or medical treatment, (2) reporting at least one cognitive-motor intervention while standing or walking, (3) use of dual-task interventions using traditional methods or modern technology to deliver a cognitive-motor task, (4) inclusion of at least one cognitive outcome. The PEDro scale was used for quality assessment. Results: A total of 1557 studies were retrieved, of which 25 studies were included in this review. Eleven studies used a technology-based dual-task intervention, while 14 trials conducted a general cognitive-motor training. The age range of the cohort was 69 to 87 years. The interventions demonstrated positive effects on global cognitive function [mean difference 0.6, 95% CI 0.29–0.90] and inhibitory control [mean difference 0.61, 95% CI 0.28–0.94]. Effects were heterogeneous (I² range: 60–95) and did not remain after a sensitivity analysis. Processing speed and dual-task costs also improved, but meta-analysis was not possible. Conclusion: Cognitive-motor and technology-based interventions had a positive impact on some cognitive functions. Dual-task interventions led to improvements of domains related global cognitive functions and inhibitory control. Likewise, technology-based exergame interventions improved functions related to processing speed, attentional and inhibitory control. Training interventions with a certain level of exercise load such as progression in difficulty and task specificity were more effective to gain task-related adaptations on cognitive functions.

Falls can have devastating consequences for older people. We determined the relationship between the likelihood of fall incidents and daily-life behavior. We used wearable sensors to assess habitual physical activity and daily-life gait quality (in terms of e.g. stability, variability, smoothness and symmetry), and determined their predictive ability for time-to-first-and-second-falls. 319 older people wore a trunk accelerometer (Dynaport MoveMonitor, McRoberts) during one week. Participants further completed questionnaires and performed grip strength and trail making tests to identify risk factors for falls. Their prospective fall incidence was followed up for six to twelve months. We determined interrelations between commonly used gait characteristics to gain insight in their interpretation and determined their association with time-to-falls. For all data -including questionnaires and tests- we determined the corresponding principal components and studied their predictive ability for falls. We showed that gait characteristics of walking speed, stride length, stride frequency, intensity, variability, smoothness, symmetry and complexity were often moderately to highly correlated (r > 0.4). We further showed that these characteristics were predictive of falls. Principal components dominated by history of falls, alcohol consumption, gait quality and muscle strength proved predictive for time-to-fall. The cross-validated prediction models had adequate to high accuracy (time dependent AUC of 0.66–0.72 for time-to-first-fall and 0.69–0.76 for -second-fall). Daily-life gait quality obtained from a single accelerometer on the trunk is predictive for falls. These findings confirm that ambulant measurements of daily behavior contribute substantially to the identification of elderly at (high) risk of falling.

Background. Gait characteristics extracted from trunk accelerations during daily life locomotion are complementary to questionnaire- or laboratory-based gait and balance assessments and may help to improve fall risk prediction. Objective. The aim of this study was to identify gait characteristics that are associated with self-reported fall history and that can be reliably assessed based on ambulatory data collected during a single week. Methods. We analyzed 2 weeks of trunk acceleration data (DynaPort MoveMonitor, McRoberts) collected among 113 older adults (age range, 65-97 years). During episodes of locomotion, various gait characteristics were determined, including local dynamic stability, interstride variability, and several spectral features. For each characteristic, we performed a negative binomial regression analysis with the participants’ self-reported number of falls in the preceding year as outcome. Reliability of gait characteristics was assessed in terms of intraclass correlations between both measurement weeks. Results. The percentages of spectral power below 0.7 Hz along the vertical and anteroposterior axes and below 10 Hz along the mediolateral axis, as well as local dynamic stability, local dynamic stability per stride, gait smoothness, and the amplitude and slope of the dominant frequency along the vertical axis, were associated with the number of falls in the preceding year and could be reliably assessed (all P < .05, intraclass correlation > 0.75). Conclusions. Daily life gait characteristics are associated with fall history in older adults and can be reliably estimated from a week of ambulatory trunk acceleration measurements.

Objective To test whether StandingTall, a home based, e-health balance exercise programme delivered through an app, could provide an effective, self-managed fall prevention programme for community dwelling older people. Design Assessor blinded, randomised controlled trial. Setting Older people living independently in the community in Sydney, Australia. Participants 503 people aged 70 years and older who were independent in activities of daily living, without cognitive impairment, progressive neurological disease, or any other unstable or acute medical condition precluding exercise. Interventions Participants were block randomised to an intervention group (two hours of StandingTall per week and health education; n=254) or a control group (health education; n=249) for two years. Main outcome measures The primary outcomes were the rate of falls (number of falls per person year) and the proportion of people who had a fall over 12 months. Secondary outcomes were the number of people who had a fall and the number who had an injurious fall (resulting in any injury or requiring medical care), adherence, mood, health related quality of life, and activity levels over 24 months; and balance and mobility outcomes over 12 months. Results The fall rates were not statistically different in the two groups after the first 12 months (0.60 falls per year (standard deviation 1.05) in the intervention group; 0.76 (1.25) in the control group; incidence rate ratio 0.84, 95% confidence interval 0.62 to 1.13, P=0.071). Additionally, the proportion of people who fell was not statistically different at 12 months (34.6% in intervention group, 40.2% in control group; relative risk 0.90, 95% confidence interval 0.67 to 1.20, P=0.461). However, the intervention group had a 16% lower rate of falls over 24 months compared with the control group (incidence rate ratio 0.84, 95% confidence interval 0.72 to 0.98, P=0.027). Both groups had a similar proportion of people who fell over 24 months (relative risk 0.87, 95% confidence interval 0.68 to 1.10, P=0.239), but the proportion of people who had an injurious fall over 24 months was 20% lower in the intervention group compared with the control group (relative risk 0.80, 95% confidence interval 0.66 to 0.98, P=0.031). In the intervention group, 68.1% and 52.0% of participants exercised for a median of 114.0 min/week (interquartile range 53.5) after 12 months and 120.4 min/week (38.6) after 24 months, respectively. Groups remained similar in mood and activity levels. The intervention group had a 0.03 (95% confidence interval 0.01 to 0.06) improvement on the EQ-5D-5L (EuroQol five dimension five level) utility score at six months, and an improvement in standing balance of 11 s (95% confidence interval 2 to 19 s) at six months and 10 s (1 to 19 s) at 12 months. No serious training related adverse events occurred. Conclusions The StandingTall balance exercise programme did not significantly affect the primary outcomes of this study. However, the programme significantly reduced the rate of falls and the number of injurious falls over two years, with similar but not statistically significant effects at 12 months. E-health exercise programmes could provide promising scalable fall prevention strategies. Trial registration ACTRN12615000138583