Unlabelled: Sarcopenia is defined by age-related reductions in muscle mass, strength, and physiological function, and it is especially prevalent among individuals with autoimmune diseases. Autoimmune disorders, characterized by immune dysregulation, cause systemic inflammation and damage to multiple tissues through unregulated immune activity. Research indicates that autoimmune diseases negatively impact skeletal muscle functions and may worsen the progression of sarcopenia. This viewpoint comprehensively discusses the pathogenesis and potential mechanism of sarcopenia in 3 autoimmune diseases: inflammatory bowel disease, rheumatoid arthritis, and type 1 diabetes mellitus. Mechanistically, chronic immune microenvironment alterations induce compartment-specific redistribution of leukocyte subsets and cytokine networks. These perturbations disrupt critical signaling pathways governing muscle protein synthesis, satellite cell activation, and mitochondrial bioenergetics, leading to impaired regeneration and accelerated sarcopenia progression. By delineating shared and distinct pathomechanisms across these models, this analysis reframes our understanding of immune-mediated muscle wasting. Beyond mechanistic insights, it establishes a translational framework for targeted therapies and highlights emerging research directions bridging immunology and age-related musculoskeletal decline.
Background: Family caregivers commonly help manage medications taken by persons living with dementia. Recent work has highlighted the importance of caregiver networks, which are multiple caregivers managing care for a single person, on managing care for persons living with dementia, especially medication management. However, less is known about the composition of caregiver networks.
Objective: The objective of this analysis was to describe the composition of caregiver networks that manage medications, the factors associated with helping with medications within caregiver networks, and whether racial or ethnic differences exist in caregiver network composition.
Methods: This cross-sectional secondary analysis used data from the National Health and Aging Trends Study (NHATS) "other person" files from 2011 to 2022. Descriptive statistics were calculated for caregivers who were identified as helping manage medications for a person with dementia. Mixed-effect logistic regression was used to determine factors associated with helping with medications among caregiver networks, with odds ratios converted to predicted probabilities using marginal standardization. A P value of .05 or less was considered statistically significant. Secondary analysis was stratified by race and ethnicity due to identified cultural differences in living situation and overall caregiver network composition.
Results: A total of 15,809 caregivers were analyzed. Of those, 3048 (19.2%) managed medications for persons living with dementia. Caregiver networks that manage medications tend to include a spouse or partner and child, at least one of whom has a college degree. Every person with dementia reported at least 1 person who managed their medications. White persons with dementia had an average of 2.4 (range 1-9) people who managed medications, while Black or African American persons with dementia had an average of 2.8 (range 1-9) and Hispanic or Latino persons with dementia had an average of 2.9 (range 1-8) people who managed medications. Spouses were most likely to manage medications across all racial and ethnic groups. In regression modeling, female gender (predicted probability [PP] 15%, 95% CI 13%-17%; P<.001), Black or African American race (PP 7%, 95% CI 4%-10%; P<.001), and Hispanic ethnicity (PP 4%, 95% CI 1%-9%; P=.04) were associated with an increased probability of helping with medications.
Conclusions: The size and composition of caregiver networks that manage medications for persons living with dementia differ by race and ethnicity but typically includes at least 2 people, one of whom has a college degree. Helping with medications was more likely among non-White family caregivers, while White patients with dementia were more likely to use paid help to manage medications.
Background: The global targets for HIV testing for achieving the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95-95 targets are still short. Identifying gaps and opportunities for HIV testing uptake is crucial in fast-tracking the second (initiate people living with HIV on antiretroviral therapy) and third (viral suppression) UNAIDS goals. Machine learning and health technologies can precisely predict high-risk individuals and facilitate more effective and efficient HIV testing methods. Despite this advancement, there exists a research gap regarding the extent to which such technologies are integrated into HIV testing strategies worldwide.
Objective: The study aimed to examine the characteristics, citation patterns, and contents of published studies applying machine learning and emerging health technologies in HIV testing from 2000 to 2024.
Methods: This bibliometric analysis identified relevant studies using machine learning and emerging health technologies in HIV testing from the Web of Science database using synonymous keywords. The Bibliometrix R package was used to analyze the characteristics, citation patterns, and contents of 266 articles. The VOSviewer software was used to conduct network visualization. The analysis focused on the yearly growth rate, citation analysis, keywords, institutions, countries, authorship, and collaboration patterns. Key themes and topics were driven by the authors' most frequent keywords, which aided the content analysis.
Results: The analysis revealed a scientific annual growth rate of 15.68%, with an international coauthorship of 8.22% and an average citation count of 17.47 per document. The most relevant sources were from high-impact journals such as the Journal of Internet Medicine Research, JMIR mHealth and uHealth, JMIR Research Protocols, mHealth, AIDS Care-Psychological and Socio-Medical Aspects of AI, and BMC Public Health, and PLOS One. The United States of America, China, South Africa, the United Kingdom, and Australia produced the highest number of contributions. Collaboration analysis showed significant networks among universities in high-income countries, including the University of North Carolina, Emory University, the University of Michigan, San Diego State University, the University of Pennsylvania, and the London School of Hygiene and Tropical Medicine. The discrepancy highlights missed opportunities in strategic partnerships between high-income and low-income countries. The results further demonstrate that machine learning and health technologies enhance the effective and efficient implementation of innovative HIV testing methods, including HIV self-testing among priority populations.
Conclusions: This study identifies trends and hotspots of machine learning and health technology research in relation to HIV testing across various countries, institutions, journals, and autho
Background: Artificial intelligence (AI) is increasingly integrated into palliative medicine, offering opportunities to improve quality, efficiency, and patient-centeredness in end-of-life care. However, its use raises complex ethical issues, including privacy, equity, dehumanization, and decision-making dilemmas.
Objective: We aim to critically analyze the main ethical implications of AI in end-of-life palliative care and examine the benefits and risks. We propose strategies for ethical and responsible implementation.
Methods: We conducted an integrative review of studies published from 2020 to 2025 in English, Portuguese, and Spanish, identified through systematic searches in PubMed, Scopus, and Google Scholar. Inclusion criteria were studies addressing AI in palliative medicine focusing on ethical implications or patient experience. Two reviewers independently performed study selection and data extraction, resolving discrepancies by consensus. The quality of the papers was assessed using the Critical Appraisal Skills Programme checklist and the Hawker et al tool.
Results: Six key themes emerged: (1) practical applications of AI, (2) communication and AI tools, (3) patient experience and humanization, (4) ethical implications, (5) quality of life perspectives, and (6) challenges and limitations. While AI shows promise for improving efficiency and personalization, consolidated real-world examples of efficiency and equity remain scarce. Key risks include algorithmic bias, cultural insensitivity, and the potential for reduced patient autonomy.
Conclusions: AI can transform palliative care, but implementation must be patient-centered and ethically grounded. Robust policies are needed to ensure equity, privacy, and humanization. Future research should address data diversity, social determinants, and culturally sensitive approaches.
Unlabelled: Online labor platforms (OLPs) have the potential to change how the workforce is allocated and managed in health care. The contracting, coordination, and communication of bookings and work assignments happen on these platforms in near real-time with no delay and without any human interactions. This perspective paper describes the worldwide trend toward OLPs in health care, gives an overview of the functioning of these platforms, and discusses the prospects and challenges for health care management. As a real-world case, the platform logic, growth and traffic of a Swiss OLP designed for temporary nurse deployment are presented. OLPs facilitate managing different work arrangements (float pools and temporary work) through (1) offering health care staff flexible work options, which in turn lowers the dropout rates of health care professionals; and (2) effectively managing internal staffing allowing human resource sharing within and across health care organizations. For health care management research, OLPs yield data that can be used to analyze the characteristics, use, and dynamics of flexible work arrangements and temporary work in health care.
Background: Physical activity and appropriate nutrition are essential for older adults. Improving physical health and quality of life can lead to healthy aging.
Objective: This study aims to investigate the long-term effects of multihealth promotion programs on the physical and mental health of older adults in communities.
Methods: A quasi-experimental method was used to recruit 112 older adults voluntarily from a pharmacy in central Taiwan between April 2021 and February 2023. Participants were divided into an experimental group receiving a multihealth promotion program and a control group with no specific intervention. The study measured frailty, nutritional status, well-being, and quality of life using standardized tools such as the Clinical Frailty Scale (CFS), Mini-Nutritional Assessment-Short Form (MNA-SF), Well-being Scale for Elders, and the EQ-5D-3L. Data were analyzed using descriptive statistics, independent t tests, Pearson correlation, and generalized estimating equations.
Results: A total of 112 participants were recruited. There were 64 (57.1%) in the experimental group and 48 (42.9%) in the control group. The experimental group exhibited significantly better quality of life (EQ-5D index) at weeks 12 (β=-.59; P=.01) and 24 (β=-.44; P=.04) compared to the control group. The experimental group muscle mass significantly increased at weeks 24 (β=4.29; P<.01) and 36 (β=3.03; P=.01). Upper limb strength improved significantly at weeks 12 (β=3.4; P=.04) and 36 (β=5; P=.01), while core strength showed significant gains at weeks 12 (β=4.43; P=.01) and 36 (β=6.99; P<.01). Lower limb strength increased significantly only at week 12 (β=4.15; P=.01). Overall physical performance improved significantly at weeks 12 (β=5.47; P<.01), 24 (β=5.17; P<.01), and 36 (β=8.79; P<.01).
Conclusions: The study's findings highlight the practical benefits of interventions, including physical and social activities and nutritional support, in enhancing the quality of life and general physical health of older adults. This study's findings have significant implications for clinical practice. These findings can aid in the establishment of effective interventions for older adults.
Trial registration: ClinicalTrials.gov NCT05412251; https://clinicaltrials.gov/study/NCT05412251.
Background: Older adults engage in increased amounts of sedentary behavior (SB), which can result in a significant decline in muscle function and overall health. An understanding of the motivational driving factors that lead older adults to engage in SB can help to create effective intervention programs.
Objective: This study aimed to determine the association between prevention and promotion focus with SB in older adults, as well as compare these associations with two factors (ie, age and BMI) that are commonly known to have an association with SB among older adults.
Methods: A cross-sectional analysis was conducted among 93 community-dwelling older adults with a mean age of 74.98 (SD 6.68) years. Prevention and promotion focus were both assessed using the Regulatory Focus Questionnaire. Correlation analysis was performed to determine the associations between prevention focus, promotion focus, age, and BMI with SB. Anderson-Darling tests confirmed nonnormal data distributions for all factors (except age); therefore, Spearman rank correlation was used to determine correlations between factors. Comparative analysis of significant correlations was performed using Fisher Z transformation.
Results: Prevention focus had the greatest statistically significant correlation with SB (ρ=0.296; P=.004), followed by BMI (ρ=0.204; P=.049). Both age (ρ=0.116; P=.27) and promotion focus (ρ=0.002; P=.99) had statistically insignificant correlations with SB, indicating no associations. The correlation between prevention focus and SB did not significantly differ from the correlation between BMI and SB (P=.51).
Conclusions: Prevention focus was found to have a weak, but significant positive association with SB in older adults. Although age and BMI have been found to have an association with SB in previous literature, age was not associated with SB in this study, while BMI had a significant but relatively weaker association with SB than that with prevention focus. However, the association found between BMI and SB did not statistically differ from the association found between prevention focus and SB. These findings suggest that older adults could be driven to engage in increased amounts of SB due to having a dominant prevention focus, which revolves around thoughts of safety and avoiding negative consequences. The recognition of this association has the potential to aid in developing intervention programs that could promote shifting from prevention to promotion focus, thereby reducing SB in older adults.
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