Technology-based measurements for screening, monitoring and preventing frailty.

BACKGROUND AND OBJECTIVE: Sensor technology, in particular wearable inertial sensors, has the potential to help researchers objectively assess the functionality of older adults. The following review provides an overview about the possible use of sensor technology to detect and prevent pre-frailty and frailty. METHOD: A systematic literature search in PubMed and the Cochrane Library was conducted. Articles were selected according to the following criteria: frail and/or pre-frail population, use of wearable and non-wearable sensor technology to measure or enhance human movements or activities of daily living and a focus on frailty assessment. RESULTS: A total of 28 publications were found. Sensor-derived parameters obtained during assessment of gait, functional performances and physical activity were reported to be relevant for screening and monitoring pre-frailty and frailty; however, current findings are limited to cross-sectional studies, which do not allow establishment of a causal relationship between motor performance, physical activity and specific frailty states. No study monitored specific activities of daily living. DISCUSSION: Outcome variables from technology-based assessment seem to provide valuable information for frailty assessment. Strenuous testing conditions as well as increased variability in gait, functional performance and physical activity may be useful in identifying frailty. Outcome variables derived from gait, motor assessment and physical activity must still be validated in large cohorts and under daily living conditions in order to develop robust screening tools for pre-frailty and frailty. Further research should focus on specific activities of daily living in pre-frail or frail older adults and technology-based approaches for intervention and prevention.

[The De Morton Mobility Index for evaluation of early geriatric rehabilitation]. / Der De Morton Mobility Index zur Evaluation der geriatrischen Frührehabilitation.

BACKGROUND: The timed up and go (TUG) test and the Tinetti test are most frequently used in Germany to document the success of rehabilitation in early geriatric rehabilitation. However, there has been some uncertainty whether these instruments can describe the entire spectrum of mobility with adequate precision and whether they can be successfully applied for repeated assessments during rehabilitation. Against this background the De Morton Mobility Index (DEMMI) has been developed and validated. OBJECTIVE: The aim of this study was to evaluate the suitability of the DEMMI compared to the TUG and Tinetti tests in early geriatric rehabilitation. MATERIAL AND METHODS: Ceiling and floor effects of the DEMMI, the TUG test, the Tinetti test and the Barthel Index (BI) were determined. Correlations were calculated using the Spearman correlation coefficient. Effects on the DEMMI were estimated through multiple linear regression. RESULTS: A total of 144 patients with an average age of 84.91 years (SD ±5.77) and 65.97 % females participated in this study. The mean value for the DEMMI was 33.45 (SD ±15.37) points at admission and 43.90 (SD ±15.55) at discharge (n = 144). Floor effects were observed in the TUG test in 84.72 % (n = 122) of patients at admission and in 60.42 % (n = 87) at discharge. The DEMMI, Tinetti test and BI showed no floor or ceiling effects. The correlation between the DEMMI and the TUG test was -0.48 at admission and -0.49 at discharge, between the DEMMI and the Tinetti test 0.75 and 0.82, respectively and between the DEMMI and the BI 0.54 and 0.66, respectively (p < 0,01). CONCLUSION: Significant correlations with the TUG test, the Tinetti test and the BI, together with the lack of floor and ceiling effects provide evidence that the DEMMI is an appropriate tool to assess mobility in early geriatric rehabilitation. The lack of floor and ceiling effects appears to make the DEMMI a superior tool compared to the TUG test.

A novel approach for discovering human behavior patterns using unsupervised methods.

BACKGROUND: As is well known, elderly people gradually lose the ability of self-care. The decline can be reflected in changes in their daily life behavior. A solution to assess their health status is to design sensor-enhanced living environments to observe their behavior, in which unobtrusive sensors are usually used. With respect to information extraction from the dataset collected by means of these kinds of sensors, unsupervised methods have to be relied on for practical application. Under the assumption that human lifestyle is associated with health status, this study intends to propose a novel approach to discover behavior patterns using unsupervised methods. METHODS: To evaluate the feasibility of this approach it was applied to datasets collected in the GAL-NATARS study. The study is part of the Lower Saxony research network Design of Environments for Aging (GAL) and conducted in subjects' home environments. The subjects recruited in GAL-NATARS study are older people (age ≥ 70 years), who are discharged from hospital to live alone again at their homes after treatment of a femoral fracture. RESULTS: The change of lifestyle regularity is measured. By analyzing the correlation between the extracted information and medical assessment results of four subjects, two of them exhibited impressive association and the other two showed less association. CONCLUSIONS: The approach may provide complementary information for health assessment; however, the dominant relationship between the change of behavior patterns and the health status has to be shown and datasets from more subjects must be collected in future studies. LIMITATIONS: Merely environmental data were used and no wearable sensor for activity detection or vital parameter measurement is taken into account. Therefore, this cannot comprehensively reflect reality.