RESUMEN
We propose the signal processing technique of calculating a cross-correlation function and an average deviation between the continuous blood glucose and the interpolation of limited blood glucose samples to evaluate blood glucose monitoring frequency in a self-aware patient software agent model. The diabetic patient software agent model [1] is a 24-h circadian, self-aware, stochastic model of a diabetic patient's blood glucose levels in a software agent environment. The purpose of this work is to apply a signal processing technique to assist patients and physicians in understanding the extent of a patient's illness using a limited number of blood glucose samples. A second purpose of this work is to determine an appropriate blood glucose monitoring frequency in order to have a minimum number of samples taken that still provide a good understanding of the patient's blood glucose levels. For society in general, the monitoring cost of diabetes is an extremely important issue, and these costs can vary tremendously depending on monitoring approaches and monitoring frequencies. Due to the cost and discomfort associated with blood glucose monitoring, today, patients expect monitoring frequencies specific to their health profile. The proposed method quantitatively assesses various monitoring protocols (from 6 times per day to 1 time per week) in nine predefined categories of patient agents in terms of risk factors of health status and age. Simulation results show that sampling 6 times per day is excessive, and not necessary for understanding the dynamics of the continuous signal in the experiments. In addition, patient agents in certain conditions only need to sample their blood glucose 1 time per week to have a good understanding of the characteristics of their blood glucose. Finally, an evaluation scenario is developed to visualize this concept, in which appropriate monitoring frequencies are shown based on the particular conditions of patient agents. This base line can assist people in determining an appropriate monitoring frequency based on their personal health profile.
Asunto(s)
Automonitorización de la Glucosa Sanguínea/métodos , Modelos Estadísticos , Procesamiento de Señales Asistido por Computador , Programas Informáticos , Automonitorización de la Glucosa Sanguínea/normas , HumanosRESUMEN
A single triaxial accelerometer has the ability to collect a large amount of continuous gait data to quantitatively assess the control of gait. Unfortunately, there is limited information on the validity of gait variability and fractal dynamics obtained from this device. The purpose of this study was to test the concurrent validity of the variability and fractal dynamic measures of gait provided by a triaxial accelerometer during a continuous 10 minute walk in older adults. Forty-one healthy older adults were fitted with a single triaxial accelerometer at the waist, as well as a criterion footswitch device before completing a ten minute overground walk. The concurrent validity of six outcome measures was examined using intraclass correlation coefficients (ICC) and 95% limits of agreement. All six dependent variables measured by the accelerometer displayed excellent agreement with the footswitch device. Mean parameters displayed the highest validity, followed by measures of variability and fractal dynamics in stride times and measures of variability and fractal dynamics in step times. These findings suggest that an accelerometer is a valid and unique device that has the potential to provide clinicians with valid quantitative data for assessing their clients' gait.
Asunto(s)
Aceleración , Acelerometría/instrumentación , Marcha/fisiología , Anciano , Anciano de 80 o más Años , Femenino , Fractales , Humanos , Masculino , ZapatosRESUMEN
PURPOSE: To compare the stride-to-stride fluctuations, regularity and symmetry of gait using a body-fixed accelerometer in a group of healthy young and healthy older adults. METHODS: Forty-one healthy young adults (24 ± 3 years) and forty-one healthy older adults (76 ± 5 years) completed a 10-min walk at a self-selected, normal walking speed while wearing a single waist-mounted tri-axial accelerometer. The following gait parameters were compared between age groups: mean step and stride time, step and stride time variability, stride time fractal scaling index and the regularity and symmetry of the acceleration pattern in the vertical, mediolateral and anteroposterior directions (unbiased autocorrelation procedure). RESULTS: Older adults displayed significantly greater step and stride time variability (p<0.05) and a lower stride time fractal scaling index (p<0.01), as well as significantly less regularity (p<0.05) and symmetry (p<0.05) of the anteroposterior accelerations. CONCLUSION: The results show that healthy older adults possess greater temporal gait variability, as well as a less-organized and repeatable pattern of variability and acceleration in the direction of motion, than younger adults. The findings also suggest the presence of an age-related decline in the anteroposterior control of gait, but not in the vertical and mediolateral control of gait.
Asunto(s)
Aceleración , Envejecimiento/fisiología , Marcha/fisiología , Acelerometría/instrumentación , Acelerometría/métodos , Adulto , Anciano , Fenómenos Biomecánicos , Femenino , Humanos , Cinética , Masculino , Adulto JovenRESUMEN
This work presents a self-aware diabetic patient software agent for representing a human diabetic patient. To develop a 24h, stochastic and self-aware patient agent, we extend the original seminal work of Ackerman et al. [1] in creating a mathematical model of human blood glucose levels in three aspects. (1) We incorporate the stochastic and unpredictable effects of daily living. (2) The Ackerman model is extended into the period of night-time. (3) Patients' awareness of their own conditions is incorporated. Simulation results are quantitatively assessed to demonstrate the effectiveness of lifestyle management, such as adjusting the amount of food consumed, meal schedule, intensity of exercise and level of medication. In this work we show through the simulation that the average blood glucose can be reduced by as much as 51% due to careful lifestyle management. Self monitoring blood glucose is also quantitatively evaluated. The simulation results show that the average blood glucose is further dropped by 25% with the assistance of blood glucose samples. In addition, the blood glucose is perfectly controlled in the target range during the simulation period as a result of joint efforts of lifestyle management and self monitoring blood glucose. This study focuses on demonstrating how human patients' behavior, specifically lifestyle and self monitoring of blood glucose, affects blood glucose controls on a daily basis. This work does not focus on the insulin-glucose interaction of an individual human patient. Our conclusion is that this self-aware patient agent model is capable of adequately representing diabetic patients and of evaluating their dynamic behaviors. It can also be incorporated into a multi-agent system by introducing other healthcare components so that more interesting insights such as the healthcare quality, cost and performance can be observed.