Essay: Gait quantification through technology

Essay details:

  • Subject area(s): Health essays
  • Reading time: 7 minutes
  • Price: Free download
  • Published on: November 25, 2015
  • File format: Text
  • Number of pages: 2
  • Gait quantification through technology
    0.0 rating based on 12,345 ratings
    Overall rating: 0 out of 5 based on 0 reviews.

Text preview of this essay:

This page of the essay has 1912 words. Download the full version above.

INTRODUCTION
The aging population is expanding and although we name it the ‘silver generation’, sounding glamorous and fun, healthy aging is not obvious for everyone. The number of adults older than 65 is growing fast, and with the improving health care the number of people with chronic diseases and multiple co-morbidities is rising [1]. Most attention is nowadays paid to diagnostics, event detection and disease control. Though, to keep health care affordable, and maintain the independence and quality of life of old adults, the focus needs to be shifted to prevention; by means of early detection of events and diseases, prior to the manifestation.
The research described in this thesis is based on two questions originated from the clinical practice. Questioning the possibilities of early identifications with technology, in populations who are each other’s extremes in terms of health status. On one hand, we focused on the identification of risk factors preluding a fall incident in residents with dementia living in a long-term care facility. On the other hand, subtle gait changes reveling aging in healthy adults were identified using the iPod Touch.
Fall prevention through technology
Background
Every year 7700 long-term care residents aged 65 years or older visit the hospital’s emergency department in the Netherlands as the consequence of an accident, 95% due to a fall incidents. Almost half of these residents needs to be hospitalized, of which 60% has a hip fracture. Additionally, 510 residents die every year because of an accident in a long-term care facility. On average the costs are 13.000 euros per accident for long-term care residents compared with 8.400 euros for non-residential old adults. Though, those numbers are already alarming, most likely this is an underestimation of the real fall related injuries in long-term care residents since these numbers represent only the accidents registered in the Dutch hospital’s emergency departments [2].
Fall incidents and related injuries represent a costly and unsolved safety issue with serious negative consequences for the independence and the quality of life of old adults. Every year 30% of the community dwelling older adults fall at least once a year, compared with 50% of elderly living in a long-term care facility. Where, old adults diagnosed with cognitive impairments fall twice as often as persons with normal cognitive capacity. Consequently, health care facilities sheltering old adults with cognitive impairments are more regularly confronted with fall incidents and much more injuries than facilities without old adults with cognitive problems. Unsurprisingly, fall prevention has a high priority on (psycho) geriatric wards in hospitals and long-term care facilities.
Fall prevention system
The recent developments in the field of technology provide new opportunities in the field of fall prevention. The development of light, small and cheap sensor systems are becoming more and more used in health care institutions [3]. These devices consist of wearable and non-wearable sensors. Wearable sensors are, like ambulant accelerometers, especially used for the detection of falls and more recently to monitor people with a high risk to fall in for instance home situations. However, for old adults with cognitive impairments, problems occur with wearable sensors that are not hidden or that require cooperation. Residents living on a psychogeriatric ward sometimes repeatedly undress themselves, they will forget to wear the sensor and consequently the fall prevention system is not working [4]. As an alternative, several non-wearable sensors are developed to detect whether elderly get out of their bed or stand up from a chair unassisted. Bed and chair alarms use infrared sensors or a pressure-sensing mat to detect whether elderly get out of bed or stand up from a chair unassisted. These sensors provide feedback to the patients in the form of a sound or verbal instructions, and alarm the nurses when a patient gets out of the bed or rises from a chair [5’7].
Information concerning factors that induce falls or increase fall risk is necessary to early identify residents at risk, and be able to develop an effective fall prevention sensor system [8,9]. Though, increased age and decreased cognitive functioning are determinants for falling, complex interactions among multiple factors underlie the many falls in old adults [10]. Factors that are supposed to have influences on falling include male sex, incontinence, psychoactive medication use, previous falls, mobility assistance, slow reaction time, muscle weakness, wandering and a loss of balance [11’15]. Long-term care residents with dementia have additional risk factors compared with those present in community-dwelling old adults [16] and the combination of simultaneously present co-morbidities, cognitive impairments, and frailty substantially increases the fall risk [17]. However, a theoretical fall risk model is still lacking because most studies focus on single factors, whereas falling is rarely the result of a single risk factor, and as the number of factors increases the related fall risk and injuries increases [18]. Moreover, falling is the result of a complex interaction of multiple intrinsic and extrinsic factors [10] these interactions are rarely studied. Thus, it is of great importance to understand the (relation between) risk factors and falls to reduce fall rate among long-term care residents with dementia [8].
Being able to identify residents with an increased fall risk will be a part of the basis of a technological sophisticated fall prevention sensor system, however, it does not offer the full solution. In addition to the capabilities of the sensor system, user acceptance is a major determinant as to whether a technological intervention succeeds or not [19’22]. To date, most technological developments aimed at fall prevention are technological driven. For new technologies to be successful in clinical practise a user-driven perspective, in which users participate in the development process, might ensures that technology is compatible with the needs of the end users [23’25]. To reduce fall incidents in a long-term care facility a fall prevention sensor system needs to be developed from a user-driven approach.
On one hand, the first steps were taken to realize a smart fall prevention system for residents with dementia living in a long-term care facility, within the INTERREG IVA program.
Gait quantification through technology
Background
Gait patterns change due to natural aging processes and certain pathologies. Gait and balance disorders become increasingly common with advancing age, occurring in 20% – 50% of adults aged 65 years and older [26]. Causes of abnormal gait in old adults include neurologic and non-neurologic disorders. Patients with Parkinson’s disease, multiple sclerosis, dementia and diabetes can be distinguished of their healthy peers by the gait patterns, as well as fallers can be distinguished from non-fallers, and frail old adults from healthy old adults. In fact, early detected gait abnormalities in old adults have been associated with a greater risk for adverse outcomes such as immobility, falls, dementia, institutionalization, and death[27,28].[2’7 4,31]
An objective instrument is necessary to identifying gait abnormalities in an early stage. Timely identification offers the possibility to provide interventions to reverse or slow the progression of gait impairment and disease revelation. Unsurprisingly, analyses of gait and postural control are becoming increasingly important for diagnostic purposes and monitoring interventions
Gait quantification device
The currently accepted clinical tests (e.g. questionnaires, fall diaries and physical performance tests) provide a global assessment of balance and gait ability, and suffer from limitations including ceiling effects and limited precision to detect small changes in balance and gait ability [29,30]. Whereas technologies, such as cameras, pressure mats and stand-alone tri-accelerometers, provide more objective and specific measures to assess gait and postural control clinicans are restrained by the costs, the specialized lab facililites and staff that are required. New opportunities arise for clinical practice (e.g. physical therapist, general practitioners and geriatrics) due to the recent development of smart devices standardly equipped with inertial sensors, such as tri-axial accelerometers. The small size, affordability, ease of use, data storage and processing capability of smart devices make accelerometer assessments more accessible.
Recording trunk acceleration is a sensitive measure to assess gait changes and provides a wide range of outcome parameters due to the different characteristics of the acceleration pattern. Based on the identification of foot contacts, amplitude magnitude, frequency content, and the self-affinity and regularity of the acceleration trajectory, information is provided about walking ability [31’34].
The validity and reliability of available stand-alone tri-accelerometers for motion analysis have been determined in numerous studies, measuring gait and posture parameters [35’37]. Although motion analysis using smart devices is an emerging and promising area, only a few studies exist that assess gait and postural control. First results are promising and show valid and reliable accelerometer data collection while walking and standing in healthy adults [38,39]. However it is unknown if the validity and test’retest reliability is different for different task conditions and/or for different age groups.
Gait assessments based on trunk accelerations are able to discriminate young healthy adults from older healthy adults, fallers from non-fallers, and healthy people from patient populations [33,40’42]. However, the majority of the studies focus on a small number of gait parameters including one or two characteristics of the accelerometer pattern, assessing the changes in gait over aging comparing old adults to matched controls [REF]. Remaining the relationship between the gait parameters based on different characteristics of the accelerometer pattern (e.g. stride, amplitude, frequency and trajectory related parameters) and age unclear. Prior to assess the subtle changes preluding a disease a frame of reference for natural healthy aging is necessary. More specific, gait parameters sensitive to changes over age need to be identified and investigated whether or not those identified gait parameters have the ability to discriminate younger adults from older adults.
On the other hand, in order to facilitate easily accessible and detailed information about changes in gait due to aging, pathology or an intervention, the possibilities of using a smart device to quantify gait were examined with support of The Institut Universitaire de France and the French national program `Programme d??Investissement d??Avenir IRT NanoElec`.
[Rispens, Weiss, van Schooten]
Outline of this thesis
This thesis explores the opportunities to identify risk factors and subtle changes preluding a dangerous situation and disease revelation through technology. Chapter 2 provides a syntheses of the effectiveness of fall prevention technologies used in intramural care facilities with respect to fall rate and fall-related injuries, false alarms, and user experience. Chapter 3 describes the fall rate, fall-related injuries and circumstances of these falls in terms of time, location and whether or not a fall was witnessed by the staff in a long-term care facility with residents with dementia. Additionally, the relationship between patient characteristics (classified into seven domains: demographics, activities of daily living (ADL) performance, mobility, cognition and behaviour, vision and hearing, medical conditions and medication use), and fall rate in these long-term care residents with dementia are presented. Chapter 4 gives an overview of the user requirements of the staff of a long-term care facility for a fall prevention system based on smart technology. Learning more about the experiences with the currently used sensor systems from the perspective of the users, as well as the specific requirements and expected effects for a new fall prevention system. Chapter 5 focused on the validity and reliability of the embedded accelerometer in the iPod Touch during gait and postural tasks under different conditions in participants over different ages. Younger, middle aged and older adults performed balance and standing tasks under different conditions (e.g. eyes open, eyes closed, concurrent cognitive dual task) while wearing a stand-alone accelerometer and the iPod Touch. Chapter 6, pursued a frame of reference for gait. The identified gait parameters sensitive to changes over age are presented and their ability to distinguish younger adults (aged 18-45) from older adults (aged 46-75) are described. Finally, a summary of the preceding chapters and general discussion is presented in Chapter 7.

...(download the rest of the essay above)

About this essay:

This essay was submitted to us by a student in order to help you with your studies.

If you use part of this page in your own work, you need to provide a citation, as follows:

Essay Sauce, Gait quantification through technology. Available from:<https://www.essaysauce.com/health-essays/essay-gait-quantification-through-technology/> [Accessed 28-02-20].

Review this essay:

Please note that the above text is only a preview of this essay.

Name
Email
Review Title
Rating
Review Content

Latest reviews: