Disease state fingerprint for fall risk assessment.

Abstract

Fall prevention is an important and complex multifactorial challenge, since one third of people over 65 years old fall at least once every year. A novel application of Disease State Fingerprint (DSF) algorithm is presented for holistic visualization of fall risk factors and identifying persons with falls history or decreased level of physical functioning based on fall risk assessment data. The algorithm is tested with data from 42 older adults, that went through a comprehensive fall risk assessment. Within the study population the Activities-specific Balance Confidence (ABC) scale score, Berg Balance Scale (BBS) score and the number of drugs in use were the three most relevant variables, that differed between the fallers and non-fallers. This study showed that the DSF visualization is beneficial in inspection of an individual's significant fall risk factors, since people have problems in different areas and one single assessment scale is not enough to expose all the people at risk.