Conectar
by Salma Ahi, Amirreza Reiskarimian, Mohammad Aref Bagherzadeh, Zhila Rahmanian, Parisa Pilban, Saeed Sobhanian
Vitamin D has been increasingly recognized for its potential role in modulating various health conditions, including diabetes and its complications. Despite growing evidence suggesting that adequate vitamin D levels may reduce the risk of developing type 2 diabetes and its associated microvascular complications, the precise nature of this relationship remains unclear. This study aims to elucidate the connection among vitamin D status, glycemic control, and microvascular complications in patients with type 2 diabetes, thereby highlighting the importance of vitamin D in diabetes management.This analytical cross-sectional study included 199 type 2 diabetic mellitus (T2DM) patients from the Jahrom city endocrinology clinic. Serum 25(OH)D levels were measured, and their microvascular complications (microalbuminuria, retinopathy, neuropathy, macroalbuminuria) and glycemic control (HbA1C) were measured and confirmed according to ADA guidelines and endocrinologist supervision. All analysis were done with SPSS software. The study enrolled 199 type 2 diabetic patients with a mean age of 56.79 ± 10.8 years, of which 63.3% were female and 57.3% had hypertension. The mean BMI was 28.91 kg/m², and 29.1% of participants had vitamin D deficiency. The prevalence of microvascular complications was 25.6% for retinopathy, 14.1% for neuropathy, and 40% for nephropathy. Vitamin D deficiency was notably higher among patients with retinopathy (37.25%), neuropathy (50%), and macroalbuminuria (56.25%). Patients with neuropathy and retinopathy had significantly lesser serum 25(OH)D concentrations compared to patients without these complications. There was a slight inverse correlation between vitamin D levels and both the urine albumin creatinine ratio (r = -0.175, p = 0.018) and HbA1C (r = -0.19, p = 0.007). Although the link between vitamin D levels and retinopathy was not statistically significant (η = 0.903, p = 0.68), the alteration in vitamin D levels was suggestively linked with neuropathy (η = 0.975, pThe transition back to work after cancer is a significant milestone for many survivors, affecting their financial stability, psychological well-being and overall quality of life. Return-to-work (RTW) process is often complicated by lingering physical and cognitive impairments, changes in self-identity and workplace dynamics. Understanding how cancer survivors navigate this process is crucial for the development of effective support systems. This study aimed to explore strategies employed by cancer survivors in managing the RTW process.
This study employed a qualitative content analysis approach to explore RTW strategies used by cancer survivors.
The study was conducted at a referral cancer centre and the workplaces of cancer survivors located in East Azerbaijan, Iran.
A total of 22 cancer survivors were selected using purposive sampling. These participants had completed primary cancer treatment and had rich and diverse RTW-related experiences. Data were collected through semi-structured, face-to-face interviews and then analysed using the inductive content analysis approach described by Graneheim and Lundman (2004).
‘Active Strategies for Returning to Work’ constituted the main theme and consisted of three categories, including assessing the situation, self-accommodation and impressing the workplace.
Cancer survivors actively engaged in RTW. They evaluate their situations before returning to work, seek to accommodate themselves to their circumstances and impress their workplaces to gain the necessary support. Healthcare providers, employers and families, as the most influential parties in the RTW process of cancer survivors, should recognise survivors’ positive strategies and provide informational, financial, emotional and occupational support.
Patient education is an integral component of advanced nursing care. However, current educational practice approaches exhibit numerous deficiencies and have not yielded favourable outcomes. The models used for educating patients with cardiovascular conditions lack specificity for these patients, and each addresses only a particular aspect of patient education. Consequently, this study aims to describe the process of designing and validating a patient education model for the cardiovascular community.
This study will employ a multilevel mixed design, encompassing ‘evidence analysis and context explanation’ and ‘validity testing’. The linking phase, namely, model design, will connect the two phases by building a preliminary model based on findings from the first phase. The evidence analysis and context explanation phase will involve three key steps. First, a scoping review will identify existing patient education processes and frameworks through a comprehensive literature search that includes qualitative and quantitative studies, review articles, mixed-methods research and developmental studies. This review aims to map existing evidence and provide an overview of current constructs in patient education, such as models, theories, frameworks, protocols and methods. Second, stakeholder experience elucidation will use conventional content analysis to explore stakeholders’ experiences, including nurses, patient education managers, physicians, patients and their families regarding current patient education practices. Third, situational analysis will evaluate human resources by assessing the performance of nurses and physicians in delivering patient education while also analysing non-human resources by examining the physical space and materials for patient education, evaluating current educational content and assessing educational outcomes. In the linking phase (model design), data collected during Phase One will be integrated to create an initial construct derived from the scoping review. This construct will be refined through content analysis and clarified using situational analysis data. The third phase (model validation) will focus on internal and external validation. For internal validation, a Delphi study will achieve expert consensus on the proposed model, involving specialists engaged in patient education who will evaluate its elements. For external validation, the model will undergo pilot testing in clinical settings to assess its utility by measuring outcomes for cardiac patients, such as self-care, quality of life, patient education satisfaction and treatment adherence. After the validation process, the final patient education model will be reviewed and finalised based on insights gained during both study phases.
This study has been approved by the Regional Ethics Committee at Tabriz University of Medical Sciences (IR.TBZMED.REC.1402.670). Dissemination will be achieved by publishing findings and depositing data in a publicly accessible repository to ensure transparency and facilitate future research.
This study aimed to use wearable technology to predict the sleep quality of family caregivers of people with dementia among underrepresented groups. Caregivers of people with dementia often experience high levels of stress and poor sleep, and those from underrepresented communities face additional burdens, such as language barriers and cultural adaptation challenges. Participants, consisting of 29 dementia caregivers from underrepresented populations, wore smartwatches that tracked various physiological and behavioral markers, including stress level, heart rate, steps taken, sleep duration and stages, and overall daily wellness. The study spanned 529 days and analyzed data using 70 features. Three machine learning algorithms—random forest, k nearest neighbor, and XGBoost classifiers—were developed for this purpose. The random forest classifier was shown to be the most effective, boasting an area under the curve of 0.86, an F1 score of 0.87, and a precision of 0.84. Key findings revealed that factors such as wake-up stress, wake-up heart rate, sedentary seconds, total distance traveled, and sleep duration significantly correlated with the caregivers' sleep quality. This research highlights the potential of wearable technology in assessing and predicting sleep quality, offering a pathway to creating targeted support measures for dementia caregivers from underserved groups. The study suggests that such technology can be instrumental in enhancing the well-being of these caregivers across diverse populations. by Mehdi Hosseinzadeh, Amir Haider, Mazhar Hussain Malik, Mohammad Adeli, Olfa Mzoughi, Entesar Gemeay, Mokhtar Mohammadi, Hamid Alinejad-Rokny, Parisa Khoshvaght, Thantrira Porntaveetus, Amir Masoud Rahmani
This paper seeks to enhance the performance of Mel Frequency Cepstral Coefficients (MFCCs) for detecting abnormal heart sounds. Heart sounds are first pre-processed to remove noise and then segmented into S1, systole, S2, and diastole intervals, with thirteen MFCCs estimated from each segment, yielding 52 MFCCs per beat. Finally, MFCCs are used for heart sound classification. For that purpose, a single classifier and an innovative ensemble classifier strategy are presented and compared. In the single classifier strategy, the MFCCs from nine consecutive beats are averaged to classify heart sounds by a single classifier (either a support vector machine (SVM), the k nearest neighbors (kNN), or a decision tree (DT)). Conversely, the ensemble classifier strategy employs nine classifiers (either nine SVMs, nine kNN classifiers, or nine DTs) to individually assess beats as normal or abnormal, with the overall classification based on the majority vote. Both methods were tested on a publicly available phonocardiogram database. The heart sound classification accuracy was 91.95% for the SVM, 91.9% for the kNN, and 87.33% for the DT in the single classifier strategy. Also, the accuracy was 93.59% for the SVM, 91.84% for the kNN, and 92.22% for the DT in the ensemble classifier strategy. Overall, the results demonstrated that MFCCs were more effective than other features, including time, time-frequency, and statistical features, evaluated in similar studies. In addition, the ensemble classifier strategy improved the accuracies of the DT and the SVM by 4.89% and 1.64%, implying that the averaging of MFCCs across multiple phonocardiogram beats in the single classifier strategy degraded the important cues that are required for detecting the abnormal heart sounds, and therefore should be avoided.To determine the frequency, timing, and duration of post-acute sequelae of SARS-CoV-2 infection (PASC) and their impact on health and function.
Post-acute sequelae of SARS-CoV-2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised.
Descriptive cross-sectional survey (n = 5163) was conducted from online COVID-19 survivor support groups who reported symptoms for more than 21 days following SARS-CoV-2 infection.
Participants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom-specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data.
Participants reported experiencing a mean of 21 symptoms (range 1–93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and “brain pressure” among the most distressing and impacting daily life.
Post-acute sequelae of SARS-CoV-2 infection can be characterised by a wide range of symptoms, many of which cause moderate-to-severe distress and can hinder survivors' overall well-being.
This study advances our understanding of the symptoms of PASC and their health impacts.
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