Frank Knoefel

State of the Art in Geriatric Rehabilitation. Part I: Review of Frailty and Comprehensive Geriatric Assessment

OBJECTIVES To increase recognition of geriatric rehabilitation and to provide recommendations for practice and future research. DATA SOURCES A CINAHL and 2 MEDLINE searches were conducted for 1980 to 2001. A fourth search used the Cochrane database. STUDY SELECTION One author reviewed the reference for relevance and another for quality. A total of 336 articles were selected. Excluded articles were unrelated to geriatric rehabilitation or were anecdotal or descriptive reports. DATA EXTRACTION The following major geriatric rehabilitation subtopics were identified: frailty, comprehensive geriatric assessment, admission screening, assessment tools, interdisciplinary teams, hip fracture, stroke, nutrition, dementia, and depression. Part I describes the first 5 subtopics on concepts and processes in geriatric rehabilitation. Part II focuses on the latter 5 subtopics of common clinical problems in frail older persons. A level-of-evidence framework was used to review the literature. Level 1 evidence was a randomized controlled trial (RCT) or a meta-analysis or systematic review of RCTs. Level 2 evidence included controlled trials without randomization, cohort, or case-control studies. Level 3 evidence involved consensus statements from experts or descriptive studies. DATA SYNTHESIS Of the 336 articles evaluated, 108 were level 1, 39 were level 2, and 189 were level 3. Recommendations were made for each subtopic. In cases in which several articles were written on the same topic and drew similar conclusions, the authors chose those articles with the strongest level of evidence, reducing the total number of references. CONCLUSIONS Frail elderly patients should be screened for rehabilitation potential. Standardized tools are recommended to aid diagnosis, assessment, and outcome measurement. The team approach to geriatric rehabilitation should be interdisciplinary and use a comprehensive geriatric assessment. Medication reviews and self-medication programs may be beneficial. Future research should address cost effectiveness, consensus on outcome measures, which components of geriatric rehabilitation are most effective, screening, and what outcomes are sustainable.

Medical Comorbidity and Rehabilitation Efficiency in Geriatric Inpatients

OBJECTIVES: To measure and describe medical comorbidity in geriatric rehabilitation patients and investigate its relationship to rehabilitation efficiency.

Integration of Smart Home Technologies in a Health Monitoring System for the Elderly

Among older adults, the challenges of maintaining mobility and cognitive function make it increasingly difficult to remain living alone independently. As a result, many older adults are forced to seek residence in costly clinical institutions where they can receive constant medical supervision. A home-based automated system that monitors their health and well- being while remaining unobtrusive would provide them with a more comfortable and independent lifestyle, as well as more affordable care. This paper presents a smart home system for the elderly, developed by the Technology Assisted Friendly Environment for the Third Age (TAFETA) group. It introduces the sensor technologies integrated in the system and develops a framework for the processing and communication of the extracted information. It also considers the acceptability and implications of this technology from the perspective of the potential occupants.

State of the art in geriatric rehabilitation. Part II: Clinical challenges

OBJECTIVES To examine common clinical problems in geriatric rehabilitation and to make recommendations for current practice based on evidence from the literature. DATA SOURCES A CINAHL database and 2 MEDLINE searches were conducted for 1980 to 2001. A fourth search was completed by using the Cochrane database. STUDY SELECTION One author reviewed the references for relevance and another for quality. A total of 336 articles were considered relevant. Excluded articles were unrelated to geriatric rehabilitation or were anecdotal or descriptive reports on a small number of patients. DATA EXTRACTION The following areas were the major geriatric rehabilitation subtopics identified in the search: frailty, comprehensive geriatric assessment, admission screening, assessment tools, interdisciplinary teams, hip fracture, stroke, nutrition, dementia, and depression. This article focuses on the latter 5 subtopics. The literature was reviewed by using a level-of-evidence framework. Level 1 evidence was a randomized controlled trial (RCT) or meta-analysis or systematic review of RCTs. Level 2 evidence included controlled trials without randomization, cohort, or case-control studies. Level 3 evidence involved consensus statements from experts, descriptive studies, or reports of expert committees. DATA SYNTHESIS Of the 336 articles evaluated, 108 were level 1, 39 were level 2, and 189 were level 3. Recommendations were made for each subtopic according to the level of evidence in the specific area. In cases in which several articles were written on a topic with similar conclusions, we selected the articles with the strongest level of evidence, thereby reducing the total number of references. CONCLUSIONS Frail older patients with hip fracture should receive geriatric rehabilitation. They should also be screened for nutrition, cognition, and depression. Older persons should receive nutritional supplementation when malnourished. If severe dysphagia occurs in stroke patients, gastrostomy tube feeding is superior to nasogastric tube feeding.

Determination of Sit-to-Stand Transfer Duration Using Bed and Floor Pressure Sequences

The duration of a sit-to-stand (SiSt) transfer is a representative measure of a persons status of physical mobility. This paper measured the duration unobtrusively and automatically using a pressure sensor array under a bed mattress and a floor plate beside the bed. Pressure sequences were extracted from frames of sensor data measuring bed and floor pressure over time. The start time was determined by an algorithm based on the motion of the center of pressure (COP) on the mattress toward the front edge of the bed. The end time was determined by modeling the foot pressure exerted on the floor in the wavelet domain as the step response of a third-order transfer function. As expected, young and old healthy adults generated shorter SiSt durations of around 2.31 and 2.88 s, respectively, whereas post-hip fracture and post-stroke adults produced longer SiSt durations of around 3.32 and 5.00 s. The unobtrusive nature of pressure sensing techniques used in this paper provides valuable information that can be used for the ongoing monitoring of patients within extended-care facilities or within the smart home environment.

Identifying Movement Onset Times for a Bed-Based Pressure Sensor Array

Movement in bed-placed pressure arrays often interferes with measurements. A method for automatically determining movement onset times from a bed-based pressure sensor array is proposed. By using control levels based on a moving average and the moving variance, the method can adapt to changing positions and postures of the bed occupant. A simulation was performed on a generated 24-hour pressure signal. The proposed algorithm accurately identified 97.4% of the simulated onset times. Experimental data from two subjects sleeping on the pad at night show similar results to the simulation. Using movement times, we may identify sleep restlessness as well as posture and position changes

Measurements of Sit-to-Stand Timing and Symmetry From Bed Pressure Sensors

Sit-to-stand (SiSt) analysis has been widely used in clinical practice to assess the risk of falls in the older adult population. This paper proposes automated algorithms for the unobtrusive measurement of SiSt timing and symmetry using bed pressure sensors. An integrated signal comprising all of the sensor outputs was analyzed to measure both the bed-departure timing and the timing of three clinical phases within the transfer. Data collected in clinical trials, along with independent clinical video analysis, verified the success of the bed-departure timing algorithm with a mean error of 0.11 s. The phase measurement algorithm showed significant differences (p <; 0.001) between younger and older adults in Phases II and III of the transfers, comparing well with studies found in recent clinical literature. The sensor outputs were then used to form sequences of pressure images, and an automated region of interest (ROI) detection algorithm was designed to extract regional signals from the hips and the hands. The final algorithm was designed to measure the symmetry of the body throughout the SiSt transfer from the extracted regional signals. A system accuracy of 93.0% was obtained for the automated symmetry classification of transfers. The techniques proposed in this paper can increase the precision and efficiency in clinical SiSt assessments. Their unobtrusive nature makes them particularly suitable for integration into a continuous monitoring system such as those required within the smart home environment.

Reliable respiratory rate estimation from a bed pressure array.

Unobtrusive sleep monitoring allows older adults to have continuous monitoring during the night in their own homes. We propose a method to reliably estimate respiratory rate using a bed-based pressure sensor array. Movements are detected prior to respiratory rate estimation and suppressed. The amount of movement during an estimate and a weighting for the estimate are used to create a reliability metric. The reliability metric is scored out of 100 for each sensor where high scores denote more reliable data. Once respiratory rates were calculated, the mean reliability metric determined the estimate reliability. Nocturnal data from a male and female participant was analyzed. Results show better accuracy and validity than both analysis without movement suppression and analysis with movement suppression but without postprocessing data fusion. While more than 50% of estimates include movement corruption, only 15% are unreliable and, moreover, removal of unreliable estimates significantly reduces estimate variance and provides validity estimation

Lying and sitting posture recognition and transition detection using a pressure sensor array

This paper demonstrates the use of a bed-based optical pressure sensor array to unobtrusively recognize sitting and lying postures as well as lie-to-sit postural transitions. Young healthy, older healthy, older post-stroke, and older post-hip-fracture participants performed a bed entry and exit routine. Data was collected using a pressure sensor array and video cameras. Lying and sitting postures and transitions were analyzed by our system and compared to video analysis from two medical students. For posture identification, eight pressure signal features and three classification techniques were compared. For transition detection, a movement detection algorithm was implemented and combined with the posture identification system. Postural detection accuracy of 100% was achievable using a combination of pressure features. Postural transition detection held a very low miss rate. Differences in measurement of transition duration between our system and video analysis were statistically insignificant.

Relative Thresholding With Under-Mattress Pressure Sensors to Detect Central Apnea

Unobtrusive pressure sensors can be used for biological monitoring and long-term health assessment in smart homes. The challenge in detecting events from smart home data is that people have different mattresses, unlike in hospitals where bedding is standardized. This paper proposes to model central apneas using an under-mattress pressure sensor as a measuring instrument. The model uses three parameters, namely, a relative threshold and two time lengths, applied to a moving variance signal. The use of a relative threshold allows apneas to be detected under a variety of different conditions and improves results compared to hard-coded thresholds. The algorithm developed herein was applied to simulated apneas collected from pressure sensors placed under nine different mattresses. The parameters determined from the training set were applied to the test set and produced classification results of 0.78 positive predictive value (PPV) if the bed occupants position is known and 0.75 PPV if the position is unknown. The use of the relative threshold approach overcomes the variability in mattress types found in smart homes.