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Most research on the relationship between diabetes and cognitive health has used data from high-income countries. This study described this relationship in India, the world’s most populous country.
Cross-sectional analysis of the baseline wave of the nationally representative Longitudinal Ageing Study in India, conducted from 2017 to 2019.
All 36 Indian states and union territories.
57 905 adults aged 45 years or older.
Scaled cognitive scores (mean of 0 and SD of 1) and cognitive impairment defined as a cognitive score 1.5 SD or below the age-matched and education-matched mean. Diabetes was defined as a self-report of a prior diabetes diagnosis made by a health professional or having a measured haemoglobin A1c ≥6.5%.
In age-adjusted and sex-adjusted models, people with diabetes had cognitive scores that were 0.24 SD higher (95% CI 0.22 to 0.26) and had a 1.2% (95% CI 0.6% to 1.7%) lower prevalence of cognitive impairment than people without diabetes. Differences persisted even when adjusting for demographic, socioeconomic and geographical characteristics. Rural versus urban residence modified the relationships of diabetes with cognitive score (p=0.001) and cognitive impairment (p=0.003). In fully adjusted models, rural respondents with diabetes had 0.05 SD (95% CI 0.03 to 0.07) greater cognitive scores and 1.6% (95% CI 0.9% to 2.4%) lower prevalence of cognitive impairment than those without diabetes. In urban areas, respondents with and without diabetes had similar cognitive scores and prevalence of cognitive impairment.
Middle-aged and older adults with diabetes living in India had better cognitive health than those without diabetes. Rural versus urban area of residence modified this relationship. Urban–rural differences, the nutrition transition and social conditions likely influenced the cross-sectional relationship between diabetes and cognitive health in India, leading to different associations than reported in other countries.
Fatigue is a common and debilitating symptom that is associated with an increased risk of mortality, dialysis initiation and hospitalisation among patients with chronic kidney disease (CKD). The aim of this study was to identify the characteristics, content and psychometric properties of patient-reported outcome measures (PROMs) used to measure fatigue in patients with CKD not requiring kidney replacement therapy (KRT).
Systematic review. The characteristics, dimensions of fatigue and psychometric properties of these measures were extracted and analysed.
We searched MEDLINE, Embase, PsycINFO and CINAHL from database inception to February 2023.
All studies that reported fatigue in patients with CKD stages 1–5 not receiving KRT.
We identified 97 studies (20 (21%) randomised trials, 2 (2%) non-randomised trials and 75 (77%) observational studies). 27 different measures were used to assess fatigue, of which three were author-developed measures. The 36-Item Short Form Health Survey (SF-36) and Kidney Disease Quality of Life – Short Form (KDQOL-SF) were the most frequently used measures (41 (42%) and 24 (25%) studies, respectively). Six (22%) measures were specific to fatigue (Chalder Fatigue Questionnaire, Functional Assessment of Chronic Illness Therapy – Fatigue Scale, Functional Assessment of Cancer Therapy-Fatigue, Fatigue Severity Scale, and author developed Chen & Ku 1998, and Hao et al 2021) while 21 (78%) included a fatigue subscale or item within a broader construct for example, quality of life. Various content domains assessed included tiredness, ability to think clearly, level of energy, muscle weakness, ability to concentrate, verbal abilities, motivation, memory, negative emotions and life participation. Only two measures (Chronic Kidney Disease Symptom Index – Sri Lanka, Kidney Symptom Questionnaire) were developed specifically for CKD, but they were not specific to fatigue. Six measures (Chronic Kidney Disease Symptom Index – Sri Lanka, Functional Assessment of Cancer Therapy – Anemia, Revised Illness Perception Questionnaire, Kidney Symptom Questionnaire, Short Form 6 Dimension and 36-Item Short Form Health Survey) had been validated in patients with CKD not requiring KRT.
PROMs used to assess fatigue in patients with CKD vary in content and few were specific to fatigue in patients with CKD not requiring KRT. Data to support the psychometric robustness of PROMs for fatigue in CKD were sparse. A validated and content-relevant measure to assess fatigue in patients with CKD is needed.
Post-COVID-19 conditions (PCC) may include pulmonary sequelae, fatigue and other symptoms, but its mechanisms are not fully elucidated.
This study investigated the correlation between fatigue and the presence of pulmonary abnormalities in PCC patients with respiratory involvement 6–12 months after hospitalisation.
Cross-sectional study.
A tertiary hospital in Brazil.
315 patients, aged ≥18 years, were considered eligible based on SARS-CoV-2 infection confirmed by reverse transcription-PCR.
Pulmonary function tests (PFT), cardiopulmonary exercise tests (CPET), chest CT and hand grip were performed. The following scales were applied: Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale, Euroqol 5 Dimensions quality of life (EQ-5D) and Hospital Anxiety and Depression Scale (HADS). Participants were divided between the fatigue group (FACIT-F≤30) and the non-fatigue group (FACIT-F>30). For the statistical analysis, the primary outcome was the difference in the diffusing capacity of the lungs for carbon monoxide (DLCO) between groups. Considered secondary outcomes were differences in PFT, CPET, chest CT, hand grip, EQ-5D and HADS.
The fatigue group had 81 patients (25.7%) against 234 (74.3%). PFT and CPET showed no significant difference in DLCO and oxygen consumption peak values between groups. The fatigue group had a lower workload (mean 55.3±21.3 watts vs 66.5±23.2 watts, p=0.003), higher breathing reserve (median 41.9% (33.8–52.5) vs 37.7% (28.9–47.1), p=0.028) and lower prevalence of ground glass opacity (60.8% vs 77.7%, p=0.003) and reticulation (36.7% vs 54.9%, p=0.005) in chest CT. The fatigue group had higher anxiety (57% vs 24%, p
Fatigue in patients with PCC 6–12 months after hospitalisation is relatively common and had weak correlation with pulmonary disorders. Our results suggested fatigue could be strongly related with peripheral disorders such as reduced musculoskeletal strength or psychosocial limitations.
This initiative utilised knowledge translation (KT) strategies, including digital storytelling (DST) as both a narrative and educational tool, to amplify voices and support trauma-informed healing for individuals living with chronic wounds. A multi-method KT approach was employed, involving: (1) patient DST; (2) a national Patient Journey conference; (3) webinars and conference sessions; (4) a social media campaign; (5) infographics and supplements and (6) an open-access digital library. Since its launch in November 2021, the initiative has garnered significant engagement. Twenty-five patients and care partners across Canada shared their wound care journeys. In June 2022, 191 patients, advocates, policymakers and healthcare providers attended the inaugural virtual Patient Journey. Additionally, 102 participants joined three Patient Journey events between June and October 2024. Patient stories received 23 012 views, and the social media campaign and infographics reached over 900 healthcare professionals, policymakers and advocates across Canada. The initiative raised awareness of the challenges faced by individuals living with wounds. Storytellers described grief, frustration and confusion, underscoring the need for person-centred wound care, timely specialised services and better healthcare navigation. Their experiences revealed care gaps, highlighting the urgent need for systemic change to promote equity and inclusivity in wound care.
by Alon Sela, Rinat Levinshtein, Shiri Shulman
Age-related macular degeneration (AMD) is a multi-factorial degenerative disease of the retina and the leading cause for vision loss in the developed world. Air pollution is considered the greatest environmental threat to public health globally. Accumulating evidence indicates that air pollution may be a modifiable risk factor for chronic eye diseases of the lens and retina, including AMD. We examined the concentration of seven air pollution particles and their influence on the prevalence of neovascular AMD in Israel. Records of patients with AMD between 2016 and 2019 were crossed with their residential areas and correlated with pollution data. AMD rates were correlated with 5 types of gas: nitrogen dioxide (NO2), nitrogen oxide (NO), carbon monoxide (CO), ozone (O3), sulphur dioxide (SO2), and particulate matter - PM2.5 and PM10. A total of 93 localities across Israel were included in the analysis. AMD rates were higher in localities with greater air pollution. NO2, NOx, and PM2.5 were positively correlated with AMD rates, while O3 was negatively correlated with AMD rates. However, analysis of the effect of all air pollutant particles combined, showed a complex and highly non-linear effect on AMD rate, with the strongest non-linearity observed for carbon monoxide. NO2, NOx, and PM2.5 contribute to higher rate of AMD in Israel while O3 seems to have a protective role (probably due to ultraviolet filtering) on AMD rates. The interaction between air pollutants and AMD seems to be complex and non-linear and should be further studied.As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results.
This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges. The scenarios were developed by expert nurses and underwent validation against current literature. Seventy-four ICU nurses participated in a simulation-based assessment involving four written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified ICU nurses for accuracy, completeness and response.
Nurses consistently achieved higher diagnostic accuracy than AI across open-ended scenarios, though certain models matched or exceeded human performance on standardized cases. Reaction times also diverged substantially. Qualitative response format differences emerged such as concision versus verbosity. Variations in GenAI models system performance across cases highlighted generalizability challenges.
While GenAI demonstrated valuable skills, experienced nurses outperformed in open-ended domains requiring holistic judgement. Continued development to strengthen generalized decision-making abilities is warranted before autonomous clinical integration. Response format interfaces should consider leveraging distinct strengths. Rigorous mixed methods research involving diverse stakeholders can help iteratively inform safe, beneficial human-GenAI partnerships centred on experience-guided care augmentation.
This mixed-methods simulation study provides formative insights into optimizing collaborative models of GenAI and nursing knowledge to support patient assessment and decision-making in intensive care. The findings can help guide development of explainable GenAI decision support tailored for critical care environments.
Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.
Background
Up to 45% of patients report cancer-related cognitive impairment (CRCI). A variety of characteristics are associated with the occurrence and/or severity of CRCI. However, an important gap in knowledge of risk factors for CRCI is the relative contribution of each factor. The multifactorial model of cancer-related cognitive impairment (MMCRCI) is a conceptual model of CRCI that can be used to evaluate the strength of relationships between various factors and CRCI.
Objectives
The purpose of this study was to use structural regression methods to evaluate the MMCRCI using data from a large sample of outpatients receiving chemotherapy (n = 1,343). Specifically, the relationships between self-reported CRCI and four MMCRCI concepts (i.e., social determinants of health, patient-specific factors, treatment factors, and co-occurring symptoms) were examined. The goals were to determine how well the four concepts predicted CRCI and determine the relative contribution of each concept to deficits in perceived cognitive function.
Methods
This study is part of a larger, longitudinal study that evaluated the symptom experience of oncology outpatients receiving chemotherapy. Adult patients were diagnosed with breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding 4 weeks; were scheduled to receive at least two additional cycles of chemotherapy; were able to read, write, and understand English; and gave written informed consent. Self-reported CRCI was assessed using the attentional function index. Available study data were used to define the latent variables.
Results
On average, patients were 57 years of age, college educated, and with a mean Karnofsky Performance Status score of 80. Of the four concepts evaluated, whereas co-occurring symptoms explained the largest amount of variance in CRCI, treatment factors explained the smallest amount of variance. A simultaneous structural regression model that estimated the joint effect of the four exogenous latent variables on the CRCI latent variable was not significant.
Discussion
These findings suggest that testing individual components of the MMCRCI may provide useful information on the relationships among various risk factors, as well as refinements of the model. In terms of risk factors for CRCI, co-occurring symptoms may be more significant than treatment factors, patient-specific factors, and/or social determinants of health in patients receiving chemotherapy. Background
Morning and evening fatigue are distinct and distressing symptoms experienced during chemotherapy that demonstrate a large amount of interindividual variability.
Objectives
The objectives of this study were to identify subgroups of patients with distinct morning and evening fatigue co-occurrence profiles and evaluate for differences among these subgroups in demographic, clinical, and symptom characteristics and quality of life.
Methods
Oncology patients (n = 1,334) completed the Lee Fatigue Scale to self-report morning and evening fatigue, six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct morning and evening physical fatigue profiles.
Results
Four distinct morning and evening fatigue profiles were identified (i.e., Both Low, Low Morning + Moderate Evening, Both Moderate, and Both High). Compared to the Both Low profile, the Both High profile was significantly younger, less likely to be married or partnered, more likely to live alone, had a higher comorbidity burden, and lower functional status. The Both High profile had higher levels of anxiety, depressive symptoms, sleep disturbance, and pain and lower levels of quality of life.
Discussion
The variability in the morning and evening severity scores among the four profiles supports the hypothesis that morning and evening fatigue are distinct but related symptoms. Clinically meaningful levels of both morning and evening fatigue were reported by 50.4% of our sample, which suggests that the co-occurrence of these two symptoms is relatively common. Patients in Both Moderate and Both High profiles experienced an extremely high symptom burden that warrants ongoing assessments and aggressive symptom management interventions. 
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