Preface

Falls and syncope in elderly patients

Falling is one of the major geriatric syndromes, with a multi-factorial pathogenesis due to age-related changes, pathological conditions and environmental hazards. Such a multi-factorial syndrome needs a standardized approach aimed at identifying risk factors. A comprehensive loco-motor, gait and standing balance, cardiovascular and neurological assessment, as well as a drugs regimens review, should be part of the routinely approach. Modification of environmental hazards, exercise training, behavioral and pharmacological treatment of specific diseases which can be the leading cause of falls, should all be part of an individualized intervention. Particular attention should be paid in the evaluation of unexplained falls, which can mask hypotensive or arrhythmic syncope.

Unique features of body segment kinematics in falls and activities of daily living (ADL) are applied to make automatic detection of a fall in its descending phase, prior to impact, possible. Fall-related injuries can thus be prevented or reduced by deploying fall impact reduction systems, such as an inflatable airbag for hip protection, before the impact. In this application, the authors propose the following hypothesis: “Thigh segments normally do not exceed a certain threshold angle to the side and forward directions in ADL, whereas this abnormal behavior occurs during a fall activity”. Torso and thigh wearable inertial sensors (3D accelerometer and 2D gyroscope) are used and the whole system is based on a body area network (BAN) for the comfort of the wearer during a long term application. The hypothesis was validated in an experiment with 21 young healthy volunteers performing both normal ADL and fall activities. Results show that falls could be detected with an average lead-time of 700 ms before the impact occurs, with no false alarms (100% specificity), a sensitivity of 95.2%. This is the longest lead-time achieved so far in pre-impact fall detection.

The purpose of this study is to investigate unique features of body segments in fall and activities of daily living (ADL) to make automatic detection of fall in its descending phase before the impact. Thus, fall-related injuries can be prevented or reduced by deploying feedback systems before the impact. In this study, the authors propose the following hypothesis: (1) thigh segment normally does not go beyond certain threshold angle to forward and sideways directions in ADL and (2) even if it does, the angular characteristics measured at torso and thigh differ from one another in ADL whereas in the case of fall, they become congruent. These two factors can be used to distinguish fall from ADL in its inception. Vicon 3-D motion analysis system was used in this study. High level of correlation between thigh and torso segments (corr > 0.99) was found for fall activities and low correlation coefficients (mean corr for lateral movements is 0.2338 and for sagittal movements is −0.665) were observed in ADL. By applying the hypothesis, all simulated falls could be detected with no false alarms and around 700 ms lead-time before the impact was achieved in pre-impact fall detection. It is the longest lead-time obtained so far in pre-impact fall detection.

There is now firm evidence to support interventions in the prevention of falls in older people, and emerging data support prevention of falls as a method of fracture prevention. This chapter discusses the epidemiology of falls, risk factors associated with an increased risk of falling, assessment of the older faller, and evidence-based approaches to the prevention of falls in the older person. Several randomized controlled trials have found that hip protectors, if worn, probably prevent hip fractures, but that poor compliance is a major issue limiting the effectiveness of this form of intervention. More data are needed to support the role of prevention of falls in preventing fractures, as well as comparative cost-effectiveness data with other evidence-based approaches to preventing fractures in an older population.