Effects of home-based exercise with expiratory muscle training on the prevention of falls and aspiration pneumonia in community-dwelling older adults

Home-based exercise can be beneficial for counteracting sedentary behavior and physical inactivity during the COVID-19 pandemic in older adults

The novel pandemic called coronavirus disease 2019 (COVID-19), as a global public health emergency, seems to be having a major impact on physical activity (PA) behaviors. Older adults are at high risk of death from the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). Health authorities around the world have been implementing preventive health measures, including quarantine and self-isolation, to mitigate the COVID-19 outbreak. This period is characterized by the cessation of outdoor exercising. During this period of lockdown, PA has been one of the rare reasons for going out in some countries. To avoid the harmful effects of periods of exercise cessation, PA could be prescribed to older adults, which is of great importance for breaking their sedentary lifestyle and improving their immunity. The present review discusses the potential impacts of the COVID-19 pandemic on sedentary behavior and physical inactivity in older adults. The importance of performing PA to reduce the harmful effects of the COVID-19 pandemic is discussed, and useful recommendations on home-based exercise for the older adults to maintain their level of independence, physical and mental health as well as their well-being are provided.

Benefits of Exercise on Influenza or Pneumonia in Older Adults: A Systematic Review

A coronavirus pandemic has recently become one of the greatest threats the world is facing. Older adults are under a high risk of infection because of weaker immune systems. Therefore, the purpose of this review is to summarize the recent scientific evidence that outlines the effects of exercise on influenza or pneumonia in older adults. An electronic literature search was conducted using the WEB OF SCIENCE, SCIENCEDIRECT and GOOGLE SCHOLAR databases using the following keywords, "Exercise," "Older adult," "Influenza," and "Pneumonia." Any randomized control trials, cross-sectional and observational studies that related to this topic were all included. Twenty studies met the eligibility criteria for this review. Thirteen randomized control trials investigated the effects of exercise on the immune responses to influenza or pneumonia vaccination: seven trials employed moderate aerobic exercise, three employed resistance exercise, and the remaining three used Asian martial arts or special home-based exercises. Five cross-sectional and two observational studies examined the associations between exercise/physical condition and influenza/pneumonia. Most of the current studies suggested that prolonged moderate aerobic exercise may help to reduce the risk of influenza-related infection and improve the immune responses to influenza or pneumonia vaccination in older adults. In addition, training in traditional Asian martial arts was also found to be beneficial. Future research should focus on the different effects of moderate and vigorous exercise on influenza-related diseases.

Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis

Gait and balance disorders are among the main predisposing factors of falls in elderly. Clinical scales are widely employed to assess the risk of falling, but they require trained personnel. We investigate the use of objective measures obtained from a wearable accelerometer to evaluate the fall risk, determined by the Tinetti clinical scale. Seventy-nine patients and eleven volunteers were enrolled in two rehabilitation centers and underwent a full Tinetti test, while wearing a triaxial accelerometer at the chest. Tinetti scores were assessed by expert physicians and those subjects with a score ≤18 were considered at high risk. First, we analyzed 21 accelerometer features by means of statistical tests and correlation analysis. Second, one regression and one classification problem were designed and solved using a linear model (LM) and an artificial neural network (ANN) to predict the Tinetti outcome. Pearson's correlation between the Tinetti score and a subset of 9 features (mainly related with standing and walking) was 0.71. The misclassification error of high risk patient was 0.21 and 0.11, for LM and ANN, respectively. The work might foster the development of a new generation of applications meant to monitor the time evolution of the fall risk using low cost devices at home.