Clinical trials: At Fall Risk
Gait or Balance Disorder Problems
The CBF Study Evaluating the Effect of Betahistine on the Cerebral Blood Flow
Betahistine may provide benefit by enhancement of Cerebral Blood Flow (CBF). This placebo-controlled pharmacodynamic treatment study will primarily explore the effect of betahistine on cerebral blood flow measured by dynamic…
- Trial name
- CBF (NCT01468285)
- Testing
- betahistine dihydrochloride
- Where
- 2 sites in Singapore
- Volunteers
- 27 people
- Sponsor
- Abbott Products
Accidental Falls, Fractures
Clinical Evaluation of a Wireless Monitoring Device to Reduce Falls in the Elderly and Others at High Risk of Falling
Falls are a public health problem of significant social and economic significance. No primary intervention devices have been shown to be effective in reducing falls and associated injuries.
- Conditions
- Accidental Falls, Fractures, Injuries
- Trial name
- NCT00249743
- Testing
- FallSaver (formerly called NOCwatch)
- Where
- United States
- Volunteers
- 50 people
- Sponsor
- NOCwatch International
Hip Injuries, Fractures, Hip
Mitigation of Major Hip Injury Due to Fall With a Smart Belt
Multi-center, comparative, non-significant risk adaptive study with retrospective controls. After providing informed consent and being screened for eligibility, intervention subjects will be prescribed and provided an…
- Conditions
- Hip Injuries, Fractures, Hip, Fall Injury, Fall Risk
- Trial name
- NCT05245097
- Testing
- Tango Belt, Standard of Care (SOC)
- Where
- 10 sites in United States
- Volunteers
- 471 people
- Sponsor
- ActiveProtective Technologies, Inc
Accidental Fall
System to Avoid Fall Events
Rinicare Ltd, a Lancaster-based SME, and University Hospitals of Morecambe Bay Trust (UHMB) will collaborate on a research study to determine the feasibility, suitability, and acceptability of the Rinicare developed SAFE (System…
- Trial name
- SAFE (NCT03418103)
- Testing
- Thermal imaging analysis of participant position in a hospital bed
- Volunteers
- 50 people
- Sponsor
- Rinicare Ltd
Frail Elderly Syndrome
A Machine Learning Algorithm to Predict Health Clinical Situations in Primary Healthcare for Frail Older Adults.
Introduction: We developed a machine learning algorithm to predict the risk of emergency hospitalization within the new 7 to 14 days with a good predictive performance (AUC=0.85).
- Trial name
- NCT06013709
- Testing
- PRESAE CARE
- Volunteers
- 1,478 people
- Sponsor
- Presage