PICOT Question
For adults greater than 65 years of age in the retirement community (P) does an exercise program (I) compared to no exercise program (C) decrease fall rates (O) within one year (Table 1)?
Prioritized List of Databases for the PICOT Question
The first database that was considered appropriate for the PICOT question was the National Registry of Fitness Facility Programs (NRFFP). The NRFFP is a database containing information about fitness facilities in the country and their programs (Thompson, 2019). This database can be used to answer the question by finding out if an exercise program decreases fall rates in older adults, which is why it was chosen as a starting point for the search. Next, the National Health Interview Survey (NHIS) data was suitable for the research because it includes information about falls, fall risk factors and prevention activities, and health status. This database would be helpful for determining whether exercise programs decrease fall rates in older adults (Tsai et al., 2020). Hence, it would also be helpful to see how much of an impact physical exercises will have on other aspects of health in older adults.
Another database I considered was Aging: A Snapshot of America (AASSA), which contains statistics about ages 65+ and their health risk behaviors, such as smoking and physical activity levels. It also contains information about how many people over 65 years old live in homes with no members over age 65 living there. This information will provide insight into whether or not exercise programs decrease fall rates in older adults. Furthermore, it will also show whether there are any issues with isolation among adults moving into retirement. Lastly, the PubMed database presents an optimal choice for the PICOT question (Table 2). The database contains a wealth of peer-reviewed literature on exercise and its effects on older adults. Moreover, the PubMed database contains a wide range of nursing and Allied Health literature, including evidence-based practice guidelines.
Search Tracker
Table 1: PICOT Question organizer
Table 2: Search tracker
Identified Level of Evidence in the Articles
Lipardo, D. S., & Tsang, W. W. (2020). Effects of combined physical and cognitive training on fall prevention and risk reduction in older persons with mild cognitive impairment: a randomized controlled study. Clinical Rehabilitation, 34(6), 773-782. Web.
This study investigates the effects of combined physical and cognitive training on fall prevention and risk reduction in older persons with mild cognitive impairment (MCI). A total of 120 older persons with MCI were randomized to either the intervention group (n=60) or the control group (n=60) (Lipardo et al., 2020). The intervention group received combined physical and cognitive training, while the control group received only physical training. The results showed that the intervention group had significantly fewer falls and fall-related injuries than the control group. The study’s result suggests that combined physical and cognitive training may be an effective intervention for fall prevention and risk reduction in older persons with MCI. The level of evidence for Lipardo et al. (2020) is highly reliable (Level 5). The study is a randomized controlled trial with a control group, which makes it more reliable than a single case report. The study shows that cognitive and physical training positively influences the prevention of falling and reduces risk in older adults with decreased mental function.
Miura, H., Sakaguchi, K., Ogawa, W., & Tamori, Y. (2021). Clinical features of 65‐year‐old individuals in Japan diagnosed with possible sarcopenia based on the Asian Working Group for Sarcopenia 2019 criteria. Geriatrics & Gerontology International, 21(8), 689-696. Web.
Based on the Asian Working Group for Sarcopenia 2019 criteria, possible sarcopenia was diagnosed in many 65‐year‐old individuals in Japan. The clinical features of these individuals were investigated in the research conducted by Miura et al. (2021). The mean age of the participants was 65.6±3.3 years, and the mean body mass index (BMI) was 23.0±3.2 kg/m2 (Miura et al., 2021). The mean appendicular skeletal muscle mass index (ASMI) was 7.1±1.1 kg/m2. The study defined sarcopenia prevalence among participants as 21.5% (Miura et al., 2021). The main clinical features of the sarcopenia group were older age, lower BMI, and higher prevalence of comorbidities (Miura et al., 2021). These findings suggest that sarcopenia is associated with older age, lower BMI, and a high of comorbidities in 65‐year‐old individuals in Japan. The level of evidence for the study conducted by Miura et al. (2021) is Level 2. The study investigates the geriatric features of elders with possible sarcopenia. The study defines that individuals with sarcopenia predisposition had higher bone mineral density than those without sarcopenia. However, other factors made it difficult to conclude the causality from the study’s results alone.
Chen, W., Jiang, Z., Guo, H., & Ni, X. (2020). Fall detection is based on key points of the human skeleton using openpose. Symmetry, 12(5), 744.a. Web.
Falls among older adults present a significant public health problem. The development of an effective fall detection system is crucial for reducing the incidence of falls. In this study, the authors propose a fall detection system based on the key points of human skeletons. The authors used the open oppose algorithm, which extracts key points from human skeletons. Furthermore, a support vector machine (SVM) was trained to classify falls and non-falls based on the extracted key points. Experiments on a publicly available dataset show that the system can predict falls with an accuracy of 87.33% (Chen et al., 2020). Thus, the level of evidence for the research by Chen et al. (2020) is Level 3. The research is a meta-analysis of studies that included healthy and older adults. The study looked at key points of the human skeleton and found that it could be used to detect falls in both groups. Thus, the system can be used to define how exercises influence the possibility of falls in older adults.
Thompson, W. R. (2019). A worldwide survey of fitness trends for 2020. ACSM’s Health & Fitness Journal, 23(6), 10–18. Web.
The worldwide survey of fitness trends for 2020 was conducted by the American College of Sports Medicine (ACSM). The survey asked over 3,000 fitness professionals worldwide to rate 33 different trends. The survey results showed that functional, bodyweight, and high-intensity interval training (HIIT) present the top three fitness trends for 2020. The article by Thompson (2019) presents an overview of fitness trends for 2020. The article provides valuable information about the current state of fitness in terms of age and gender, as well as trends in fitness locations, activity types, and exercise frequency. The authors provide evidence for the rise in the number of people participating in aerobics classes over the last six years. The research associates the rise with technological improvements that make it easier for people to access different types of activities. In the hierarchy of evidence, the research by Thompson (2019) can be acknowledged as Level 6 evidence because it features experts’ opinions.
Tsai, Y. J., Yang, P. Y., Yang, Y. C., Lin, M. R., & Wang, Y. W. (2020). Prevalence and risk factors of falls among community-dwelling older people: results from three consecutive waves of the national health interview survey in Taiwan. BMC geriatrics, 20(1), 1-11. Web.
Falls present a significant problem for the global population of older people. In the research by Tsai et al. (2020), the authors study the prevalence of falls and risk factors for falls using data from three waves of the National Health Interview Survey in Taiwan. Results showed that the prevalence of falls increased with age and that risk factors for falls included gender, chronic diseases, physical activity, and poor eyesight. In addition, the study determined that elders who previously experienced falls were most likely to experience higher levels of depression and anxiety. Additionally, the researchers found that those who fell in the previous year are more likely to be experiencing physical disability or vision problems such as cataracts or glaucoma. The research by Tsai et al. (2020) can be acknowledged as level 1 evidence because the study presents a systematic review of data from three consecutive surveys.
References
Chen, W., Jiang, Z., Guo, H., & Ni, X. (2020). Fall detection is based on key points of the human skeleton using openpose. Symmetry, 12(5), 744.a. Web.
Lipardo, D. S., & Tsang, W. W. (2020). Effects of combined physical and cognitive training on fall prevention and risk reduction in older persons with mild cognitive impairment: a randomized controlled study. Clinical Rehabilitation, 34(6), 773-782. Web.
Miura, H., Sakaguchi, K., Ogawa, W., & Tamori, Y. (2021). Clinical features of 65‐year‐old individuals in Japan diagnosed with possible sarcopenia based on the Asian Working Group for Sarcopenia 2019 criteria. Geriatrics & Gerontology International, 21(8), 689-696. Web.
Thompson, W. R. (2019). A worldwide survey of fitness trends for 2020. ACSM’s Health & Fitness Journal, 23(6), 10–18. Web.
Tsai, Y. J., Yang, P. Y., Yang, Y. C., Lin, M. R., & Wang, Y. W. (2020). Prevalence and risk factors of falls among community-dwelling older people: results from three consecutive waves of the national health interview survey in Taiwan. BMC geriatrics, 20(1), 1-11. Web.