Background: Optimal application of the recently updated World Health Organization (WHO) guidelines for exercise in advanced age necessitates an accurate adjustment for the age-related increasing variability in biological age and fitness levels, alongside detailed recommendations across a range of motor fitness components, including balance, strength, and flexibility. We previously developed and validated a novel tool, designed to both remotely assess these fitness components, and subsequently deliver a personalized exercise program via smartphone. We describe the design of a prospective randomized control trial, comparing the effectiveness of the remotely delivered personalized multicomponent exercise program to either WHO exercise guidelines or no intervention.
View Article and Find Full Text PDFBackground: The World Health Organization has recently updated exercise guidelines for people aged >65 years, emphasizing the inclusion of multiple fitness components. However, without adequate recognition of individual differences, these guidelines may be applied using an approach that "one-size-fits-all." Within the shifting paradigm toward an increasingly personalized approach to medicine and health, it is apparent that fitness components display a significant age-related increase in variability.
View Article and Find Full Text PDFBackground: There is a need for standardized and objective methods to measure postural instability (PI) and gait dysfunction in Parkinson's disease (PD) patients. Recent technological advances in wearable devices, including standard smartphones, may provide such measurements.
Objectives: To test the feasibility of smartphones to detect PI during the Timed Up and Go (TUG) test.
Sensors (Basel)
November 2019
Gait disorders and falls are common in elders and in many clinical conditions, yet they are typically infrequently and subjectively evaluated, limiting prevention and intervention. Completion-time of the Timed-Up-and-Go (TUG) test is a well-accepted clinical biomarker for rating mobility and prediction of falls risk. Using smartphones' integral accelerometers and gyroscopes, we already demonstrated that TUG completion-time can be accurately measured via a smartphone app.
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