The Effects of an Advanced Movable Restraint Design on the Elderly Residents at Long-Term Care Facilities in Taiwan

Purpose: The purpose of this study was to evaluate the effects, both initially and after 6 months, of an “advanced movable restraint” with openended palm sleeve restraint bands for the elderly residents at long-term care facilities in northern Taiwan. Background. Elderly residents in long-term care facilities are often forced to remain bed-ridden by traditional bed restraint bands due to their irritable, confused conditions and the associated risks of self-extubating their nasogastric (NG) tubes, urinary catheters, etc. However, the traditional bed restraint bands can themselves lead to further physical and mental complications such as skin damage, depression, hostility, and even rhabdomyolysis, increasing the risk of death. Design. Quasiexperimental design. Methods: This parallel-design study was conducted with elderly residents at eight long-term care facilities. The newly designed advanced movable restraint featuring movable open-ended palm sleeve restraint bands was applied to the elderly residents in the experimental group, allowing them greater freedom of movement such that they were not required to remain bed-ridden. In contrast, the elderly residents in the control group were restrained with traditional bed restraints requiring that they remain bedridden. The following four instruments and indicators were then used to compare the effects of the two types of restraints: (1) an activities of daily living (ADL) survey based on the Barthel Index, (2) a muscle power test, (3) an exercise frequency and duration survey, and (4) self-extubation rates. The effects of the interventions were tested by using the t test or chi-square test to compare pre-test results for the ADL survey, muscle power test, exercise frequency and duration survey, and self-extubation rates to those at a 6-month follow-up. Results: A total of 80 elderly residents were included in the experimental group, while 80 elderly residents were included in the control group. At the 6-month follow-up, the residents restrained with the advanced movable restraint had a significantly increased mean muscle power score (χ2 =17.212, P < 0.001), significantly decreased self-extubation rate (χ2 =40.733, P < .001), and significantly increased exercise frequency and duration per week (χ2=27.095 P < 0.001; 26.241 P < 0.001). Conclusions: This study showed that the advanced movable restraint can improve muscle power scores, self-extubation rates, and exercise frequencies and durations by allowing residents greater freedom of movement without the need to remain bed-ridden. It is thus crucial to use such advanced movable restraints and develop standardized technology systems to support the elderly residents and nurses in long-term care facilities

Personalized Risk Assessment in Never, Light, and Heavy Smokers in a prospective cohort in Taiwan.

The objective of this study was to develop markedly improved risk prediction models for lung cancer using a prospective cohort of 395,875 participants in Taiwan. Discriminatory accuracy was measured by generation of receiver operator curves and estimation of area under the curve (AUC). In multivariate Cox regression analysis, age, gender, smoking pack-years, family history of lung cancer, personal cancer history, BMI, lung function test, and serum biomarkers such as carcinoembryonic antigen (CEA), bilirubin, alpha fetoprotein (AFP), and c-reactive protein (CRP) were identified and included in an integrative risk prediction model. The AUC in overall population was 0.851 (95% CI = 0.840-0.862), with never smokers 0.806 (95% CI = 0.790-0.819), light smokers 0.847 (95% CI = 0.824-0.871), and heavy smokers 0.732 (95% CI = 0.708-0.752). By integrating risk factors such as family history of lung cancer, CEA and AFP for light smokers, and lung function test (Maximum Mid-Expiratory Flow, MMEF25-75%), AFP and CEA for never smokers, light and never smokers with cancer risks as high as those within heavy smokers could be identified. The risk model for heavy smokers can allow us to stratify heavy smokers into subgroups with distinct risks, which, if applied to low-dose computed tomography (LDCT) screening, may greatly reduce false positives