Background: Physical activity (PA) has been shown to improve total mortality and colorectal-specific mortality risk; however, colorectal cancer (CRC) survivors have lower rates of PA compared with survivors with other types of cancers. Objective: To examine the effect of PA interventions on CRC survivors. Methods: A systematic review and meta-analysis were conducted to identify randomized controlled trials that met the inclusion criteria, which included an intervention designed to increase PA and more than 1 outcome of interest. Random effects of the meta-analyses were performed using Review Manager 5.3. Results: Eight publications representing 7 randomized controlled trials of 803 participants were identified. All studies used a combination of behavioral change methods. Physical activity interventions significantly improved disease-specific quality of life, PA level, and maximum amount of oxygen and did not show significant improvements for fatigue and body mass index among CRC survivors. Conclusions: We provided evidence that PA interventions were effective in improving disease-specific quality of life, PA level, and maximum amount of oxygen; however, they did not improve fatigue and body mass index. Further randomized controlled trials are needed to determine the optimal mode of delivering PA intervention for CRC survivors. Implications for Practice: As the survival rate of patients with CRC increases, survivors of CRC need to increase PA in a community setting after completing primary treatments. Effective and efficient modes of PA intervention delivery could improve health-related outcomes and address specific barriers for CRC survivors.

Marijuana is the most common illicit substance globally. The rate of marijuana use is increasing in young adults in the US. The current environment of legalizing marijuana use is further contributing to an increase of users. The purpose of this study was to explore the characteristics of adults who abuse marijuana (20–49 years old) and analyze behavior and social relation variables related to depression and suicide risk using machine-learning algorithms. A total of 698 participants were identified from the 2019 National Survey on Drug Use and Health survey as marijuana dependent in the previous year. Principal Component Analysis and Chi-square were used to select features (variables) and mean imputation method was applied for missing data. Logistic regression, Random Forest, and K-Nearest Neighbor machine-learning algorithms were used to build depression and suicide risk prediction models. The results showed unique characteristics of the group and well-performing prediction models with influential risk variables. Identified risk variables were aligned with previous studies and suggested the development of marijuana abuse prevention programs targeting 20–29 year olds with a regular depression and suicide screening. Further study is suggested for identifying specific barriers to receiving timely treatment for depression and suicide risk.

This secondary analysis used data collected for the Korean Longitudinal Study of Aging from 2006 to 2018 to examine changes in life satisfaction among middle-aged adults living alone in South Korea. Individuals who were over 45 years of age, lived alone at the time of the first data collection wave, and responded at least twice to the survey over the 12-year study period were included in the final linear mixed model (N = 124). Life satisfaction increased for those who had increased assets, were widowed, and had more frequent contact with acquaintances (i.e., once a month and once a week compared with once a year). Life satisfaction decreased as the number of chronic illnesses increased for underweight individuals compared with normal weight or overweight individuals and for depressed versus non-depressed individuals. This study’s findings indicate that increased social support is beneficial for middle-aged marginalized individuals, including those who are economically disadvantaged, have few social interactions, are underweight, and have chronic illnesses.

There has been a growing emphasis on data across various health-related fields, not just in nursing research, due to the increasing volume of unstructured data in electronic health records (EHRs). Natural Language Processing (NLP) provides a solution by transforming this unstructured data into structured formats, thereby facilitating valuable insights. This methodology paper explores the application of NLP in nursing, using an exemplar case study that analyzes YouTube data to investigate social phenomena among adults living alone. The methodology involves five steps: accessing data through YouTube’s API, data cleaning, preprocessing (tokenization, sentence segmentation, linguistic normalization), sentiment analysis using Python, and topic modeling. This study serves as a comprehensive guide for integrating NLP into nursing research, supplemented with digital content demonstrating each step. For successful implementation, nursing researchers must grasp the fundamental concepts and processes of NLP. The potential of NLP in nursing is significant, particularly in utilizing unstructured textual data from nursing documentation and social media. Its benefits include streamlining nursing documentation, enhancing patient communication, and improving data analysis.

Objectives: This study explored the experiences, challenges, and social dynamics of middle-aged individuals living alone in South Korea, compared with English-speaking individuals. From a public health perspective, this study examined cultural and generational factors influencing wellbeing.Methods: This cross-sectional descriptive study used topic modeling with natural language processing to analyze YouTube video comments related to single-person households collected from 30 Korean and 88 English-language channels.Results: Distinct cultural and generational differences emerged. Korean participants, particularly women, frequently expressed concerns about social stigma surrounding marriage and singlehood. English-speaking participants emphasized careers and personal independence. Themes included employment, financial security, travel, and food. Younger Koreans showed interest in leisure activities (mukbang, solo drinking). Older Koreans expressed concerns about economic stability and societal expectations.Conclusion: Understanding the cultural and generational context of living alone is essential for developing public health strategies addressing social isolation, mental health, and health equity. Digital platforms function as alternative social spaces, offering insights into socially isolated individuals’ needs. Public health interventions should consider online engagement to support wellbeing and reduce health disparities in this population.