Performing dual-task (DT) activities is essential for independent living among elderly people. No study has investigated motor performance in various cognitive-motor DT activities, utilizing the Timed Up and Go (TUG) test. This study aimed to compare motor performance between four cognitive-motor DT tests in community-dwelling older adults. The sample consisted of 60 older women. The cognitive tasks performed with the TUG test were (a) mental calculation, (b) memory recall, (c) verbal fluency, and (d) reaction to a stimulus. Lower limb muscle strength was assessed with the 30-Second Chair Stand Test, balance with the Four Square Step Test, and balance confidence with the Activities-specific Balance Confidence Scale. Completion times and DT costs were calculated. Mental calculation (r = 0.63, p < 0.01) and verbal fluency (r = 0.65, p < 0.01) tasks were similarly correlated with the TUG test, and significantly impacted motor performance compared to other DT tests. The reaction to a stimulus test showed a high relationship with the TUG test (r = 0.89, p < 0.01) and had the least impact on motor performance. These findings suggest that the cognitive task type can significantly influence motor performance during DT activities. Adding a cognitive load to the TUG test may improve its ability to identify older adults at risk for falls, aiding in the development of targeted interventions. Further research is required to validate these findings.

Introduction: Chronic non-specific neck pain is a musculoskeletal disorder which may affect psychological well-being and work performance. Depression, anxiety, and limitations in daily and work activities may occur differently between genders in individuals with chronic non-specific pain. The main purpose of the present cross-sectional study was to investigate the differences between men and women with chronic non-specific neck pain in disability due to pain, quality of life, and sedentary behavior. Secondly, we investigated the intercorrelations between the three above measured variables in the total sample. Methods: Eighty patients (44 men and 36 women), aged 20–55 years (Μ = 33.55, SD = 11.16) with chronic non-specific neck pain in the last 3 years participated. They completed three validated questionnaires which measured neck disability pain, quality of life, and sedentary behavior with (a) the Neck Disability Index (NDI), (b) the quality-of-life EuroQol-5D (EQ-5D), and (c) the International Physical Activity Questionnaire (IPAQ), respectively. Gender comparisons with Kruskal–Wallis H tests and correlation analysis with Spearman r tests were performed between the above variables using SPSS 29.00. Results: Men reported (a) better quality of life (H = 16.14, p < 0.001), (b) lower pain-related disability (H = 13.96, p < 0.001), (c) more time spending in vigorous physical activity (H = 3.37, p < 0.05), (d) more time spending in moderate physical activity (H = 5.17, p < 0.05), and (e) more time spending in walking (H = 11.24, p < 0.001). A strong positive correlation was found between the NDI and the EQ-5D Index (r = 0.74, p = 0.002) and a negative correlation was found between NDI and the EQ-5D VAS (r = −0.65, p = 0.003). Discussion: The present findings reported that men have more time walking, and have lower disability due to neck pain than women, thus they have better quality of life than women with chronic non-specific neck pain.

Background: Patients with primary central nervous system (CNS) tumors often experience fatigue and sleep disturbances, significantly impacting their quality of life. Exercise has been shown to improve these symptoms in various cancer populations. The aim of this study is to evaluate the effects of different types of exercise on fatigue and sleep in less-investigated CNS tumor patients. Methods: A literature search was conducted in PubMed, Scopus, Cochrane Library, and CINAHL. Eligible randomized and non-randomized studies evaluating exercise interventions in patients diagnosed with primary brain tumors were systematically reviewed, primarily using a narrative synthesis approach. Cancer-related fatigue and sleep-related outcomes were extracted as variables of interest. Where possible [≥2 Randomized Control Trials (RCTs) available for glioma patients], meta-analyses were conducted to assess the overall effects of physical therapy on the above-mentioned outcomes. Results: A total of 15 relevant intervention studies were identified, either RCTs or other types of studies, such as prospective feasibility cohort studies and case studies. A total of 448 participants were enrolled, with the majority diagnosed with glioma. There were single reports on pituitary adenoma after surgery and meningioma patients. In glioma patients, the overall effect of various modality exercise interventions on fatigue was non-significant, reflecting the heterogeneous characteristics of studies with diverse outcomes. However, meta-analysis focusing on combined exercise interventions (aerobic and resistance training) showed a positive effect on reducing fatigue in these patients [Standardized Mean Difference (SMD) = 0.866, p = 0.03]. Fatigue in glioma patients may also improve through yoga and Pilates. Aerobic but not strength exercise seems to improve sleep in glioma patients (SMD = 1.14, p = 0.02). Sleep quality may also improve through yoga and combined exercise. Conclusions: Certain types of exercise appear to effectively reduce fatigue and improve sleep in patients with CNS tumors. Future, well–controlled, multi-arm, larger-scale studies are necessary to resolve discrepancies, as well as to explore long-term outcomes and define factors influencing individualized exercise responses.

Introduction: There is controversy about ChatGPT’s (OpenAI, San Francisco, CA) potential to answer anatomy questions and play a significant role in anatomy education. We aimed to assess ChatGPT’s ability to locate and summarize literature about its performance in answering anatomy questions. We also aimed to explore Gemini’s (Google, Mountain View, CA) ability to perform this task. Methods: We asked ChatGPT and Gemini to list and summarize five studies: 1) about ChatGPT’s ability to answer anatomy questions, 2) comparing ChatGPT with another artificial intelligence (AI) tool in terms of answering anatomy questions, and 3) showing that ChatGPT answered anatomy questions with success lower than 70%. For each query, we measured how many studies were 1) correctly identified and 2) accurately summarized, and we assessed the presence of any bias. Results: ChatGPT performed excellently in the first query (100%/100%), poorly in the second (20%/20%), and moderately in the third query (60%/40%). It conducted a strict self-evaluation and did not hallucinate. Gemini’s performance was 100%/40%, 60%/40%, and 80%/60% in the three queries, respectively. It was inflexible in finding a variety of publications, while it often hallucinated and exhibited bias against ChatGPT. Conclusions: ChatGPT and Gemini generated reliable responses only when they were simply asked to detect studies about ChatGPT’s ability to answer anatomy questions. They particularly struggled to locate and outline comparative studies. While Gemini performed generally better, it hallucinated and exhibited bias against ChatGPT. Ongoing evolution may enhance ChatGPT and Gemini’s potential to contribute to anatomical research.

Introduction Artificial intelligence has recently garnered increased research interest in orthopedics; yet, its role in the exploration of orthopedic literature remains unknown. We aimed to evaluate the ability of ChatGPT and ScholarGPT to identify and outline literature on orthopedic navigation and the visualization of related surgical anatomy. Methods We asked ChatGPT and ScholarGPT to list and summarize five studies: 1) about augmented reality-based navigation in orthopedic surgery, 2) about how well augmented reality-based navigation enabled anatomical accuracy in orthopedic surgery, and 3) which compared augmented reality-based navigation with another navigation technique in orthopedic surgery. Regarding each query, we evaluated how many studies were correctly detected and accurately summarized. Results ChatGPT scored excellently in identifying studies across all three queries. However, its performances in accurately summarizing these studies were 40%, 60%, and 60% respectively. On the other hand, ScholarGPT’s performances in identifying were 60%, 40%, and 0%, respectively, while in summarization, they were 0%, 20%, and 0%, respectively. Both platforms exhibited bias in favor of augmented reality-based navigation across all queries. Conclusion ChatGPT and ScholarGPT are not yet able to provide researchers with reliable data from the literature on orthopedic navigation and related surgical anatomy. Ongoing artificial intelligence development may essentially reinforce these platforms’ potential to play a more significant role in orthopedic research.