The authors performed statistical analysis using predictive analytics tools for healthcare and care applications (PATH). PATH, however, has been used not only for statistics but also for machine learning signal processing. It played an essential role in fixing falls and calculating the probability of further falls. Sensors were attached to the patients’ waists to calculate statistical probabilities. The authors also noted the low statistical power for detecting differences between falling and not falling. The statistics qualified the patient as falling if they fell at least once during the experiment. To compare the results, the authors used paired u-tests. The authors show descriptive statistics in diagrams. The study participants were, on average, about 85 years old. One of the most important statistical methods was questionnaires that patients were able to complete. These questionnaires demonstrated their weakness and, in general, the possibility for the authors to participate in the study (Ziegl et al., 2020). After completing the questionnaires, it was found that not all prospective patients could participate in the trial.
Initially, more than 45 people were supposed to participate in the experiment, but many of them refused to do this or had health problems that prevented them from doing so. Of the 39 participants in the study, 23 people were falling (Ziegl et al., 2020). A total of 23 people fell more than 70 times, and the average number of falls was three to five times. The authors are satisfied with the function of the TUG signal, as it separated the fallen from the non-fallen; all statistical results are reflected in graphs and charts.
I wonder if it would be possible to conduct a study on precisely the same topic without using all the authors’? If yes, what methods? All data obtained by the researchers was obtained through sophisticated equipment, which is not present in all nursing homes. Reading this study, which is entirely dependent on sophisticated technological medical equipment, one wonders if it would have been possible to conduct such a study ten years ago. The second question is about screening and how exactly was screening done. What would happen if screening was done more often? Perhaps this would increase the number of falling patients from 23 people. However, there is no doubt about screening itself as a data collection method.
The authors note the typical limitations for studies using TUG; this limitation is the ability to predict falls. In addition, the authors note that the TUG may experience unusual launches, which, perhaps, is appropriate to call failures (Ziegl et al., 2020). The algorithm needs to be adjusted for the conditions of use. Limitations of the study may also include the difficulty in using TUG and, in particular, the difficulty in using TUG at home. Such devices are easy to use by a team of scientists. Still, it will be problematic for the elderly, inexperienced medical workers, and relatives of the elderly to find out the forecasts and timing of the TUG.
The article does not make recommendations for future research but provides examples of research that has already been done with hundreds of older people. Studies with small samples are also beneficial, especially for testing specific devices, but studies with many older people are much more valuable and reliable. The first recommendation for future research is to increase the sample size. The second recommendation is to move the study out of nursing homes and into homes where relatives live with their elderly. The theme of falling in homes can be closely intertwined with aging in place and making the aging of relatives living together comfortable. I am very interested in this topic, so I would like to connect this research and similar research to aging in place.
Reference
Ziegl, A., Hayn, D., Kastner, P., Löffler, K., Weidinger, L., Brix, B., Goswami, N., & Schreier, G. (2020). Quantitative falls risk assessment in elderly people: Results from a clinical study with distance based timed up-and-go test recordings. Physiological Measurement, 41(11). Web.