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BackgroundFrailty is a health condition related to aging and dependence. A reduction in or delay of the frailty state can improve the quality of life of the elderly. However, providing frailty assessments can be difficult because many factors must be taken into account. Usually, measurement of these factors is performed in a noncentralized manner. Additionally, the lack of quantitative methods for analysis makes it impossible for the diagnosis to be as complete or as objective as it should be.ObjectiveTo develop a centralized mobile system to conduct elderly frailty assessments in an accurate and objective way using mobile phone capabilities.MethodsThe diagnosis of frailty includes two fundamental aspects: the analysis of gait activity as the main predictor of functional disorders, and the study of a set of frailty risk factors from patient records. Thus, our system has several stages including gathering information about gait using accelerometer-enabled mobile devices, collecting values of frailty factors, performing analysis through similarity comparisons with previous data, and displaying the results for frailty on the mobile devices in a formalized way.ResultsWe developed a general mechanism to assess the frailty state of a group of elders by using mobile devices as supporting tools. In collaboration with geriatricians, two studies were carried out on a group of 20 elderly patients (10 men and 10 women), previously selected from a nursing home. Frailty risk factors for each patient were collected at three different times over the period of a year. In the first study, data from the group of patients were used to determine the frailty state of a new incoming patient. The results were valuable for determining the degree of frailty of a specific patient in relation to other patients in an elderly population. The most representative similarity degrees were between 73.4% and 71.6% considering 61 frailty factors from 64 patient instances. Additionally, from the provided results, a physician could group the elders by their degree of similarity influencing their care and treatment. In the second study, the same mobile tool was used to analyze the frailty syndrome from a nutritional viewpoint on 10 patients of the initial group during 1 year. Data were acquired at three different times, corresponding to three assessments: initial, spontaneous, and after protein supplementation. The subsequent analysis revealed a general deterioration of the subset of elders from the initial assessment to the spontaneous assessment and also an improvement of biochemical and anthropometric parameters in men and women from the spontaneous assessment to the assessment after the administration of a protein supplement.ConclusionsThe problem of creating a general frailty index is still unsolved. However, in recent years, there has been an increase in the amount of research on this subject. Our studies took advantage of mobile device features (accelerometer sensors, wireless communication capabilities, and processing capacities among others) t...

Frailty refers to a condition in which elderly people have medical and social problems, determining the index of vulnerability. This process begins by presenting a reduction in the ability to care for oneself. It is caused by characteristics like shrinking, weakness, poor endurance, slowness or low activity levels. In this work we present a proposal for frailty monitoring through the control of rehabilitation and daily activities. After the frailty index diagnosis, elderly people are continuously monitoring by using a mobile phone-accelerometer enabled. Only, carrying this device all day, it is possible to control daily activities, recommending rehabilitation exercises and checking whether there could be a risk according to the frailty index.

This article presents an easy-to-deploy and low-cost Internet of Things infrastructure for gait characterization based on a set of wireless inertial sensors, called nodes, connected to the same local area network. These nodes allow acquiring inertial raw data from the trunk of each frail elder involved in explicit gait trials carried out directly in the elderly care homes. The Internet of Things infrastructure has been validated for Quantitative Gait Analysis showing an adequate accuracy in the demarcation of relevant gait events and in the estimation of stride interval variability. The latter, in combination with other characteristics that are commonly used to assess the state of frail elders and which come from anthropometric, biological, nutritional, functional, and mobility domains, allows us to perform a cross-sectional cohort study and a subsequent multiple logistic regression to evaluate their impact on cognitive functioning. The cohort study and the multivariate regression are performed using a sample of 81 frail elders from two nursing homes in Spain. The results obtained indicate that frail elders aged 90 years or older, with moderate dependence in daily functioning and with a stride interval gait variability greater than 6%, were most likely to suffer cognitive impairment, representing what is called cognitive frail.

This paper presents a cross-sectional study to analyze the impact on cognitive decline of a set of characteristics used for frailty assessment in elderly people. Considered characteristics come from several dimensions, including anthropometric, biological, nutritional, functional and mobility. Cognitive functioning is estimated by the Mini-Mental State Examination test. Additionally, mobility dimension is assessed from two perspectives: one based on direct observation of ambulation through subjective gait analyses; and the other performing explicit gait trials by using the instrumentation provided. In order to accomplish the purpose of this research, a multiple logistic regression analysis is carried out. Variables are grouped according to popular and/or standardized categories adopted in other clinical studies. Mini-Mental State Examination represents the dependent variable, while the characteristics for frailty assessment make up the set of explanatory variables. The multiple logistic regression is performed using a sample of 81 frail elders from two nursing homes in Spain. The results obtained indicate that frail elders aged 90 years of older, with moderate dependence in daily functioning, moderate risk of falls and with a stride interval gait variability greater than 6% were most likely to suffer cognitive decline, representing what is called cognitive frails.