Abstract
Falls and fall-related injuries can have devastating health consequences and form a growing economic burden for the healthcare system. To identify individuals at risk for preventive measures and therapies, fall risk assessment scores have been developed. However, they are costly in terms of time and effort and rely on the subjective interpretation of a skilled professional making them less suitable for frequent assessment or in a screening situation.
Small wearable sensors as activity monitor can objectively provide movement information during daily-life tasks. It is the aim of this study is to evaluate whether the activity parameters from wearable monitors correlate with fall risk scores and may predict conventional assessment scores.
Physical activity data were collected from nineteen home-dwelling frail elderly (n=19, female=10; age=81±5.6 years, GFI=5.4±1.9, MMSE=27.4±1.5) during waking hours of 4 consecutive days, wearing a wearable 9-axis activity monitor (56×40×15mm, 25g) on the lateral side of the right thigh. The signal was analysed using self-developed, previously validated algorithms (Matlab) producing the following parameters: time spent walking, step count, sit-stand-transfer counts, mean cadence (steps/min), count of stair uses and intensity counts >1.5G.
Conventional fall risk assessment was performed using the Tinetti sore (range: 0–28=best), a widely used tool directly determining the likelihood of falls and the Short Physical Performance Battery (SPPB, range: 0–12=best) which measures lower extremity performance as a validated proxy of fall risk. The anxiety to fall during activities of daily living was assessed using the self-reported Short Falls Efficacy Scale-International (FES-I, range: 7–28=worst).
Correlations between activity parameters and conventional scores were tested using Pearson's r.
The activity parameters (daily means) for the 19 participants were 70.8min (SD=28.7; min-max= 22.8–126.6) of walking, 4427 steps (SD=2344; min-max= 1391–8269) with a cadence 79.3 steps per minute (SD=17.1; min-max=52.8–103.9) and 33.3 sit-stand transfers (SD=9.7; min-max=8.8–48.0).
The average Tinetti score was 21.2 (SD=5.1; min-max=10.0–27.0), with SPPB scoring 7.8 (SD=2.4; min-max=3.0–12.0), and FES-I 4.6 (SD=5.1; min-max=7.0–23.0).
Strong (r≥0.6) and significant correlations existed between the walking cadence and the Tinetti (r=.60, p=<.01) and SPPB (r=.71, p=<.01) scores. No other correlations were found between the activity parameters and the Tinetti, SPPB and none with the psychological FES-I questionnaire.
Conventional fall risk scores and activity data are comparable to literature values and thus representative of home-dwelling frail elderly including a wide range covered for both dimensions.
No quantitative activity measure had a predictive value for fall risk assessment. Strongly correlated with Tinetti and SPPB, objectively measured cadence as a qualitative parameter seems a useful parameter for remotely identifying fall risk in frail elderly. The perceived anxiety to falls was not correlated to quantitative and qualitative activity parameters suggesting that this psychological aspect hardly affects activity.
Wearable activity monitors seem a valid tool to assess fall risk remotely and thus allow low cost, frequent and large group screening of frail elderly towards a health economically viable tool for a growing societal need. The predictive quality of activity monitored data may be increased by deriving additional qualitative measures from the activity data.
