No abstract

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.

BackgroundComputer-based cognitive stimulation applications can help the elderly maintain and improve their cognitive skills. In this research paper, our objectives are to verify the usability of PESCO (an open-software application for cognitive evaluation and stimulation) and to determine the concurrent validity of cognitive assessment tests and the effectiveness of PESCO’s cognitive stimulation exercises.MethodsTwo studies were conducted in various community computer centers in the province of Granada. The first study tested tool usability by observing 43 elderly people and considering their responses to a questionnaire. In the second study, 36 elderly people completed pen-and-paper and PESCO tests followed by nine cognitive stimulation sessions. Meanwhile, a control group with 34 participants used computers for nine non-structured sessions.ResultsAnalysis of the first study revealed that although PESCO had been developed by taking usability guidelines into account, there was room for improvement. Results from the second study indicated moderate concurrent validity between PESCO and standardized tests (Pearson’s r from .501 to .702) and highlighted the effectiveness of training exercises for improving attention (F = -4.111, p < .001) and planning (F = 5.791, p < .001) functions.ConclusionsPESCO can be used by the elderly. The PESCO cognitive test module demonstrated its concurrent validity with traditional cognitive evaluation tests. The stimulation module is effective for improving attention and planning skills.