Clinical Research Directory

Examining Nurses' Trust and Acceptance of FAIR, an AI-powered Falls Risk Recommender

An exploratory mixed-method study will be conducted to test acceptance and trust of an AI-powered falls risk predictor system by inpatient hospital nurses

Validating a Multifactorial Algorithm to Predict Fall Risk in Community Dwelling Older Persons Aged 65 Years or Older

Falls in older adults have an annual incidence between 23 and 34% for persons of 65 years and older. The lack of transfer of promising research results on falls prevention to daily practice may have several explanations. One of the first concerns for that matter may be a valid and sensitive selection of the persons at risk who are eligible for fall prevention interventions Goals of the prospective obeservational study: 1. To validate a new, comprehensive, multifactorial screeningsintrument to predict the risk on falls in community dwelling, Belgian, older adults (\>65years or older). 2. To optimize the new, comprehensive, multifactorial screeningsintrument and to specify correct cut off scores Researchers will compare the data of the accuracy of new algorithm with the current Belgian policy. Participants will participate in a one-time testing moment, where they will be asked some questions about general characteristics and medical history, followed by two questionnaire and seven physical tests . Based on this data, their fall risk will be assessed. In the year that follows, the older person's fall behavior will be monitored with a monthly falls calender. Participant will be asked to fill out these calenders everyday ('Did you fall today?' YES/NO) and send them at the end of each month to the researchers, digitally or postal.