Improved Screening of Fall Risk using Free-Living based Accelerometer Data

Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively

Improved screening of fall risk using free-living based accelerometer data

Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively

Anti-inflammatory effects of a new tumour necrosis factor-alpha (TNF-α) inhibitor (CNI-1493) in collagen-induced arthritis (CIA) in rats

A recently developed compound, a multivalent guanylhydrazone (CNI-1493) that inhibits TNF-α production by suppressing TNF-α translational efficiency, was administered in an experimental model of collagen type II-induced arthritis in DA rats. CNI-1493 was injected daily intraperitoneally either before the onset of arthritis or after the establishment of clinical disease. Prophylactic treatment with CNI-1493 significantly prevented or delayed the onset and suppressed the severity of arthritis in a dose-dependent manner. Therapeutic intervention with CNI-1493 in established joint disease also resulted in a significant reduction of clinical signs of arthritis in treated animals. No severe side-effects were noted when animals were treated with daily CNI-1493 doses up to 5 mg/kg. An immunohistochemical study was performed which demonstrated that CNI-1493 led to a reduced expression of TNF-α at the site of disease activity. Thus, CNI-1493 with documented inhibitory effects on TNF-α synthesis, has proven successful in ameliorating the course of arthritis in CIA. We believe that the use of a compound such as CNI-1493 with a defined mode of action provides a useful tool for dissecting and understanding important pathogenic mechanisms operating in the development of chronic arthritis