Arthritis is associated with health challenges. Lifestyle traits are believed to influence arthritis development and progression; however, data to support personalized treatment regimens based on holistic lifestyle factors are missing. This study aims to provide a comprehensive list of associations between lifestyle traits and the health status of individuals with arthritis in the Canadian population, using binary logistic regression analysis on data from the Canadian Community Health Survey, which includes 104,359 respondents. Firstly, we explored the association between arthritis and various aspects of health status including self-reported lifestyle factors. Secondly, we examined the associations between self-reported dietary intake and smoking status with general, mental, and oral health, and sleep disturbance among individuals both with and without arthritis. Our analysis revealed that individuals with arthritis reported considerably poorer general, mental, and oral health, and poorer sleep quality compared to those without arthritis. Associations were also found between self-reported dietary intake and various measures of health status in individuals with arthritis. Smoking and exposure to passive smoking were associated not only with arthritis but also with compromised sleep quality and poorer general, mental, and oral health in people with and without arthritis. This study highlights the need for personalized and holistic approaches that may include a combination of dietary interventions, oral health improvements, sleep therapies, and smoking cessation for improved arthritis prevention and care.

YouTube is increasingly used by medical and health science students as a supplementary learning tool. However, the quality and educational value of surface anatomy videos on YouTube remain underexplored. This study aimed to systematically evaluate the quality, reliability, and educational usefulness of YouTube videos focusing on human surface anatomy. A structured YouTube search was conducted (December 2024–January 2025), targeting the seven primary body regions with specific keywords (e.g., “surface anatomy,” “bone landmarks,” and “dermatomes”). The top 30 videos per search term were selected. Two anatomists independently assessed each video using the Anatomy Content Score (ACS), Global Quality Scale (GQS), modified DISCERN (mDISCERN), and Journal of the American Medical Association (JAMA) benchmarks. Inter‐observer agreement was evaluated via Kappa coefficient. Associations between video quality scores and YouTube metrics (view count, like ratio, interaction index) were examined using nonparametric tests. Among 1050 retrieved videos, 85 (8%) met inclusion criteria; 48 (56.5%) were classified as “useful” (ACS ≥ 13, GQS ≥ 4). Longer video duration was significantly ( p < 0.001) associated with higher usefulness, whereas view count, like ratio, and interaction index did not correlate with usefulness. ACS strongly correlated with GQS ( r _s = 0.754) and both correlated moderately with mDISCERN. No significant differences in video quality were observed across body regions, search rankings, presented material type, or upload period (pre‐ vs. post‐COVID‐19). YouTube offers a moderate‐quality resource for learning surface anatomy, with approximately 60% of evaluated videos deemed useful. Popularity metrics are unreliable indicators of video educational quality, underscoring the need for peer‐reviewed, high‐quality digital resources.

Academic performance in medical education is influenced by multiple factors, including learning strategies and study behaviors. However, the extent to which these factors are associated with academic performance remains unclear. This study aimed to describe the frequency of learning strategies and examine their associations with academic performance among medical students, with emphasis on evidence-based strategies. A cross-sectional anonymous online survey was conducted among medical students at Jordan University of Science and Technology. Learning strategies were assessed using ten items rated as rarely, sometimes, or often. Composite indices were constructed for overall, evidence-based, and common/passive strategies. Academic performance was categorized as excellent, very good, or good or below. Group comparisons were performed using chi-square tests and ANOVA, and multivariable logistic regression identified factors associated with excellent performance. Among 556 respondents, 52.2% reported excellent performance. Evidence-based strategies showed a graded increase across performance groups and were independently associated with higher odds of excellent performance (aOR 1.47, 95% CI 1.16–1.86), whereas common/passive strategies were not. These findings suggest that evidence-based learning strategies are associated with better academic performance, supporting their potential role in medical education. However, the findings are exploratory rather than causal.

Background: Sleep disturbances and oral health problems are both prevalent public health concerns with substantial impacts on quality of life and systemic health. However, population-based evidence examining the independent associations between multiple dimensions of oral health and multiple sleep outcomes remains limited. This study investigated the associations between oral health indicators and sleep disturbances among Canadian adults using nationally representative survey data. Methods: We utilized data from adults aged 18 years and older who completed the 2017–2018 Canadian Community Health Survey (CCHS). Four oral health indicators were assessed: self-reported oral health, oral pain, bleeding gums, and dry mouth. Four sleep outcomes were examined: short sleep duration, insomnia symptoms, unrefreshing sleep, and difficulty staying awake. Multivariable logistic regression models were used, with progressive adjustment for sociodemographic factors, health status, and health behaviors. Results: All four oral health indicators were significantly associated with most sleep outcomes in the full adjusted model. For short sleep duration, the strongest association was observed for poor oral health (AOR: 1.623). For insomnia symptoms, dry mouth (AOR: 1.904) and oral pain (AOR: 1.826) exhibited the strongest associations. For unrefreshing sleep, oral pain (AOR: 1.937) and dry mouth (AOR: 1.808) were associated with higher odds of reporting unrefreshing sleep. For difficulty staying awake, dry mouth (AOR: 1.720) and oral pain (AOR: 1.701) showed the strongest associations. Conclusion: Oral health problems are associated with multiple dimensions of sleep disturbance. These findings highlight the need for integrated care and complementary screening between dental and sleep medicine.

Background: Artificial intelligence (AI) tools are increasingly being integrated into medical education. However, their use in anatomy learning, particularly among medical students in the Middle East, remains underexplored. Objective: To assess medical students’ perceptions, attitudes, and patterns toward AI use for anatomy learning, and to examine associations with sex, academic level, and GPA. Methods: A cross-sectional study was conducted among medical students at Jordan University of Science and Technology. A validated 36-item questionnaire (Cronbach’s α = 0.785–0.867) was distributed online. Responses were analyzed using descriptive and inferential statistics, including chi-square tests and Spearman’s rank correlation, to identify factors associated with AI use and perceptions of AI in anatomy learning. Results: Among 351 respondents, 300 (85.5%) use AI for anatomy, with 95.7% of them using it during exam preparation. ChatGPT was the dominant platform (96.0%). Gross anatomy was the most studied topic. Students strongly endorsed AI platforms for understanding concepts, immediate answers, 24/7 availability, and time-saving. Major concerns included a lack of visual/lab experience, premium costs, and AI misunderstanding questions. Overall, among the AI-using subgroup, 75.3% of students supported the formal integration of AI, but 52.3% believed AI could replace aspects of traditional teaching. Two gender-related patterns were observed in the unadjusted analyses, but neither remained statistically significant after FDR correction. Higher academic performance was associated with lower agreement that AI could replace traditional teaching or function as a standalone learning resource. Conclusions: Medical students use AI as an anatomy learning adjunct, valuing its conceptual and accessibility benefits while recognizing its inability to replace hands‑on dissection. Higher GPA was only associated with lower agreement that AI could serve as a complete standalone resource for anatomy learning. These findings support structured AI literacy training, equitable access, and balanced curricula AI integration that preserves hands-on foundations.