Clinical Health Assessment Research Articles

Bridging School and Practice? Barriers to the Integration of 'Boundary Objects' for Learning and Assessment in Clinical Nursing Education.

In clinical health professions education, portfolios, assignments and assessment standards are used to enhance learning. When these tools fulfill a bridging function between school and practice, they can be considered 'boundary objects'. In the clinical setting, these tools may be experienced as time-consuming and lacking value. This study aimed to investigate the barriers to the integration of boundary objects for learning and assessment from a Cultural-Historical Activity Theory (CHAT) perspective in clinical nursing education. Nineteen interviews and five observations were conducted with team leads, clinical educators, supervisors, students, and teachers to obtain insight into intentions and use of boundary objects for learning and assessment. Boundary objects (assessment standards, assignments, feedback/reflection/patient care/development plan templates) were collected. The data collection and thematic analysis were guided by CHAT. Barriers to the integration of boundary objects included: a) conflicting requirements in clinical competency monitoring and assessment, b) different application of analytical skills, and c) incomplete integration of boundary objects for self-regulated learning into supervision practice. These barriers were amplified by the simultaneous use of boundary objects for learning and assessment. Underlying contradictions included different objectives between school and practice, and tensions between the distribution of labor in the clinical setting and school's rules. School and practice have both convergent and divergent priorities around students' clinical learning. Boundary objects can promote continuity in learning and increase students' understanding of clinical practice. However, effective integration requires for flexible rules that allow for collaborative learning around patient care.

Heterogeneous effects of blood pressure screening

This is the first study that investigates the heterogeneous effects of blood pressure (BP) screening on subsequent changes in BP in a high-income country. We use data from clinical health assessments carried out in 2010 (baseline) and 2014 (follow-up) as part of a nationally-representative longitudinal study on ageing in Ireland. We focus on individuals who at baseline do not report a previous hypertension diagnosis and employ a Regression Discontinuity Design by comparing outcomes at follow-up on either side of the BP cutoff that separates normal to abnormal BP at baseline. We find that the BP screening reduces BP at follow-up, with larger and more precisely estimated effects for males, middle-age individuals (as opposed to older individuals), and individuals without public health insurance coverage.

Validation of the Health Assessment Tool (HAT) based on four aging cohorts from the Swedish National study on Aging and Care

BackgroundAs global aging accelerates, routinely assessing the functional status and morbidity burden of older patients becomes paramount. The aim of this study is to assess the validity of the comprehensive clinical and functional Health Assessment Tool (HAT) based on four cohorts of older adults (60 + years) from the Swedish National study on Aging and Care (SNAC) spanning urban, suburban, and rural areas.MethodsThe HAT integrates five health indicators (gait speed, global cognition, number of chronic diseases, and basic and instrumental activities of daily living), providing an individual-level score between 0 and 10. The tool was constructed using nominal response models, first separately for each cohort and then in a harmonized dataset. Outcomes included all-cause mortality over a maximum follow-up of 16 years and unplanned hospital admissions over a maximum of 3 years of follow-up. The predictive capacity was assessed through the area under the curve (AUC) using logistic regressions. For time to death, Cox regressions were performed, and Harrell’s C-indices were reported. Results from the four cohorts were pooled using individual participant data meta-analysis and compared with those from the harmonized dataset.ResultsThe HAT demonstrated high predictive capacity across all cohorts as well as in the harmonized dataset. In the harmonized dataset, the AUC was 0.84 (95% CI 0.81–0.87) for 1-year mortality, 0.81 (95% CI 0.80–0.83) for 3-year mortality, 0.80 (95% CI 0.79–0.82) for 5-year mortality, 0.69 (95% CI 0.67–0.70) for 1-year unplanned admissions, and 0.69 (95% CI 0.68–0.70) for 3-year unplanned admissions. The Harrell’s C for time-to-death throughout 16 years of follow-up was 0.75 (95% CI 0.74–0.75).ConclusionsThe HAT is a highly predictive, clinically intuitive, and externally valid instrument with potential for better addressing older adults’ health needs and optimizing risk stratification at the population level.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis.

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Oral health status, related behaviours and perceived stress in xerostomia, Sicca and Sjögren's syndromes patients - a cross-sectional study.

Mouth dryness increases the risk of some oral health-related conditions. Furthermore, it is unclear if patients with dry mouth engage in appropriate oral health-related behaviours. The study examined oral health, related behaviours, and perceived stress in dry-mouth patients and compared them to matched controls without mouth dryness. Information about 182 dry-mouth patients and 302 age- and sex-matched subjects was retrieved. Three dry mouth groups: xerostomia, Sicca syndrome and Sjögren's syndrome, were formed based on patient self-reported and objectively assessed symptoms. The World Health Organization's Oral Health for Adults and Perceived Stress Scale (PSS-10) questionnaires inquired about sociodemographic characteristics, oral health-related behaviours, and self-perceived stress. Clinical oral health assessments included: caries experience measured as total numbers of decayed (DS), missing (MS), filled surfaces (FS), number of remaining teeth, erosive tooth wear and extent of periodontal pocketing. Data were analyzed using bivariate and multivariable tests. The dry-mouth participants had higher mean (SD) DMFS scores than their matched controls: xerostomia patients vs. controls: 74.6 (34.4) and 66.3 (35.4), Sicca syndrome patients vs. controls: 88.3 (34.0) and 70.1 (33.9), and Sjögren's syndrome patients vs. controls: 95.7 (31.5) and 74 (33.2). In comparison to controls, individuals with Sicca and patients with Sjögren's syndromes had lower mean (SD) number of remaining teeth, 15.9 (10.1) vs. 21.7 (8.4) and 13.8 (10.0) vs. 20.1 (9.2), and a lower mean (SD) extent of periodontal pocketing, 20.7 (28.6) vs. 41.1 (31.0), and 21.2 (24.1) vs. 34.8 (34.2), respectively. Xerostomia, Sicca syndrome and Sjögren's syndrome patients had higher odds of using fluoridated toothpaste; OR 1.8 (95%CI 1.1-2.9), OR 5.6 (95%CI 1.7-18.3) and OR 6.9 (95%CI 2.2-21.3), respectively. Participants with Sjögren's syndrome had lower odds of the last dental visit being within the last year; OR 0.2 (95%CI 0.1-0.8). Dry-mouth patients had higher caries experience and fewer teeth than comparison groups but a lower extent of periodontal pocketing. Even though more participants with dry mouth used fluoridated toothpastes, their oral health-related behaviours were not optimal.

APN nurses' core competencies for general clinical health assessment in primary health care. A scoping review.

The field of Advanced Practice Nursing (APN) has developed over the past six decades. However, the definition of roles and responsibilities of APN nurses seem to be contested due to both a lack of a clear definition of the concept and to institutional and cultural barriers that restrict the nurses' opportunities to practise to the full extent of their competencies. The objective of this scoping review was to identify, examine and conceptually map the available literature on APN nurses' core competencies for general health assessment in primary health care. We performed a scoping review, following the methodological guidance for reporting as it is described by the Joanna Briggs Institute (JBI). Furthermore, the PRISMA-ScR statement and checklist for reporting scoping reviews were followed. Guiding the initial process for the search, we used the Population, Concept and Context mnemonic (PCC) to clarify the focus and context of the review. We found three areas of core competencies on which APN nurse draw in performing general health assessments in primary health care: (1) 'Collaborative, leadership and management skills' (2) 'Person-centred nursing care skills' and (3) 'Academic and educational skills'. Furthermore, we found that the three areas are interrelated, because it is crucial that APN nurses draw on collaborative competencies related to leadership and management to meet the service users' needs and deliver high-quality and person-centred care. There is a need for a more specific investigation into how APN nurses' core competencies play a role during general health assessments of patients in primary care. We suggest an evaluation of what works for whom in what circumstances looking into the interrelation between competencies, skills and knowledge when an APN nurse performs a general health assessment in a primary healthcare setting.

COMPARISON OF SELF-RATED HEALTH, FRAILTY-INDEX, AND MULTIMORBIDITY AS HEALTH INDICATORS

Abstract Self-rated health (SRH) predicts mortality and other adverse events (e.g. hospitalizations, intensive care), and reflects clinical biomarker levels even in individuals without diseases. We hypothesize SRH captures information relevant to clinical decision-making, with minimal effort, and might be used prior to more intensive and focused health assessments. To explore the clinical utility of SRH, we 1) analysed its overlap with clinical health assessments, multimorbidity, and Rockwood frailty-index (FI), and 2) compared their performances in mortality prediction. Our data came from two population-based cohort studies in Finland: Health 2000 Survey (H2000: n=6,329, aged 30-97 years) and National FinHealth 2017 Study (FH17: n=4,105, aged 30-98 years). Cox regression adjusted for age and sex, and Harrel’s C-index were used to assess the performances of continuous SRH, FI, and multimorbidity in 4-years-all-cause mortality prediction. Of those with fair/poor SRH, 85% in H2000 and 91% in FH2017 were also frail (FI≥0.25), and 91% and 81% had at least two diseases, respectively. Baseline SRH (H2000: HR[95CI]=3.14[2.10-4.69], FH17: HR[95CI]=4.57[2.37-8.83]), FI (H2000: HR[95CI]=1.40[1.28-1.53], FH17: HR[95CI]=1.54[1.32-1.80]) and number of diseases (H2000: HR[95CI]=1.18[1.10-1.27], FH17: HR[95CI]=1.18[1.03-1.35]) predicted death, and their C-indices were 0.879, 0.884, 0.877 in H2000 and 0.797, 0.800, 0.779 in FH17, respectively. SRH is concordant with clinical health indicators, such as multimorbidity and FI, and predicts mortality equally well in community-dwelling adults. The analyses are ongoing and we will further assess the utility of SRH in other cohorts, using other outcomes, and clinical indicators from registers, and perform age-, sex- and disease-stratified analyses.

Experience with Accelerometer Physical Activity Tracking in Adults and Adolescents with Hemophilia A: Tool Validation Using Results from the XTEND-1 Efanesoctocog Alfa Phase 3 Trial

Introduction Wearable physical activity trackers have become increasingly common within clinical studies, patient monitoring, and health assessments. Physical activity is a key endpoint of interest when evaluating treatments for hemophilia. The objective of this analysis was to describe the psychometric properties of five outcome measures of physical activity from the accelerometer activity monitor used in the XTEND-1 study. Methods XTEND-1 (NCT04161495) was a Phase 3, open-label, multicenter study of the efficacy, safety, and pharmacokinetics of efanesoctocog alfa in previously treated adults and adolescents aged ≥12 years with severe hemophilia A. Patients receiving prior standard-of-care prophylaxis factor VIII (FVIII) received once-weekly (qw) prophylactic efanesoctocog alfa for 52 weeks (Arm A); patients on prior on-demand FVIII treatment received efanesoctocog alfa on-demand for 26 weeks, then prophylactic efanesoctocog alfa qw for 26 weeks (Arm B). Patients wore a triaxial medical grade accelerometer activity monitor device for continuous monitoring during pre-specified intervals 8 consecutive days after (or before) scheduled visits (to collect data for a full 7-day week + 1 additional day): after the screening, baseline, Week 4, 13, 26, 39 visits, and before the Week 52 visit. Compliance was assessed based on number of monitoring intervals and number of days wearing the device, and number of minutes spent wearing the device within those days. Five outcomes were assessed: light physical activity (LPA), moderate-to-vigorous activity, sedentary behavior (minutes/day spent in these individual outcomes out of total number minutes wearing device while awake), steps (number of steps taken per day), and metabolic equivalent of a task (MET; total estimated metabolic rate per day). Psychometric analyses of these outcomes using data from Arm A were conducted to evaluate test-retest reliability (TRTR), concurrent validity, known-groups validity, ability to detect change, and meaningful within-patient change (MWPC) of physical activity measurements ( Table 1). An analysis to detect potential influential outlier patients for each outcome that could potentially bias results, was conducted. Results In total, 132 out of 133 patients in Arm A and 26 out of 26 patients in Arm B, distributed in 19 countries, had accelerometer data. Compliance was defined post-hoc as ≥12 consecutive wear hours/day and results are presented for days achieving this threshold; no meaningful differences in results were found when incorporating number of monitoring intervals or number of days within the interval in the compliance definition. Most patients maintained or improved physical activity as measured by the activity monitors, with an overall increase in mean (SD) minutes during each monitoring period for LPA (baseline: 636 [234]; Week 52: 653 [237]), though there were variable changes throughout the study period. Measurements of LPA demonstrated acceptable TRTR, concurrent validity with three validation criteria, and ability to detect change with all or most of the anchor variables, while demonstrating good separation for improvement and deterioration thresholds to provide for MWPC ( Table 1). Two patients were identified as influential outliers for LPA. For moderate-to-vigorous activity, the concurrent validity showed that two validators met the criteria, and for steps, four validators met the criteria. Sedentary behavior and MET did not meet criterion with any of the validators ( Table 1). Out of the outcomes assessed, LPA showed the best psychometric properties within the study. Conclusion These analyses show how physical activity tracking through wearables was used to assess outcomes in patients with severe hemophilia A, who switched to a FVIII replacement therapy that provided normal to near-normal FVIII activity for most of the weekly dosing interval, resulting in reduced pain and improved physical functioning as assessed by patient-reported outcome measures. Analyses of these results revealed limitations in the psychometric properties of the assessed outcomes. To further understand the personal motivation and barriers for improving physical activity, addition of goal-setting could be considered in future clinical studies.

Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach

BackgroundComorbid conditions are very common in rheumatoid arthritis (RA) and several prior studies have clustered them using machine learning (ML). We applied various ML algorithms to compare the clusters of comorbidities derived and to assess the value of the clusters for predicting future clinical outcomes.MethodsA large US-based RA registry, CorEvitas, was used to identify patients for the analysis. We assessed the presence of 24 comorbidities, and ML was used to derive clusters of patients with given comorbidities. K-mode, K-mean, regression-based, and hierarchical clustering were used. To assess the value of these clusters, we compared clusters across different ML algorithms in clinical outcome models predicting clinical disease activity index (CDAI) and health assessment questionnaire (HAQ-DI). We used data from the first 3 years of the 6-year study period to derive clusters and assess time-averaged values for CDAI and HAQ-DI during the latter 3 years. Model fit was assessed via adjusted R2 and root mean square error for a series of models that included clusters from ML clustering and each of the 24 comorbidities separately.Results11,883 patients with RA were included who had longitudinal data over 6 years. At baseline, patients were on average 59 (SD 12) years of age, 77% were women, CDAI was 11.3 (SD 11.9, moderate disease activity), HAQ-DI was 0.32 (SD 0.42), and disease duration was 10.8 (SD 9.9) years. During the 6 years of follow-up, the percentage of patients with various comorbidities increased. Using five clusters produced by each of the ML algorithms, multivariable regression models with time-averaged CDAI as an outcome found that the ML-derived comorbidity clusters produced similarly strong models as models with each of the 24 separate comorbidities entered individually. The same patterns were observed for HAQ-DI.ConclusionsClustering comorbidities using ML algorithms is not computationally complex but often results in clusters that are difficult to interpret from a clinical standpoint. While ML clustering is useful for modeling multi-omics, using clusters to predict clinical outcomes produces models with a similar fit as those with individual comorbidities.

Health behaviour profiles in young Australian adults in relation to physical and mental health: The Raine Study.

We aimed to identify latent health behaviour profiles of young adults and examine their associations with physical and mental health outcomes. We also characterised the profiles by socio-demographic characteristics. Data were collected between 2012 and 2014. Participants (N = 476) were young adults (M age [SD] = 22.1 [.57] years) from Generation 2 of the Raine Study longitudinal cohort. Health behaviours were measured via ActiGraph GT3X waist monitors (physical activity, sedentary behaviour) and questionnaires (diet quality, alcohol, smoking and sleep). Physical and mental health were measured using clinical health assessments, blood biomarkers, and questionnaires. Latent Profile Analysis using Mplus (8.2) was employed to identify profiles. Four latent profiles were identified: 'heavy drinkers with moderately unhealthy eating habits' (high takeaway foods; n = 135), 'unhealthy food abstainers' (low takeaway foods; n = 138), 'moderately sedentary alcohol abstainers' (n = 139) and 'physically active drinkers with unhealthy eating habits' (high takeaway foods and sugary drinks; n = 64). 'Physically active drinkers with unhealthy eating habits' had the poorest (physical and mental) health outcomes, yet the lowest insulin resistance. 'Unhealthy food abstainers' had the most favourable health outcomes (adiposity, health perceptions, blood pressure). Sex differed among the profiles. The profiles identified among young adults are different to profiles with general adult populations. A novel finding was that 'physically active drinkers with unhealthy eating habits' had low insulin resistance. The findings also suggest that future interventions may need to be sex specific. Our findings suggest that health behaviour interventions for young adults should be targeted to distinct profile characteristics.

The Use of Pictograms in the Evaluation of Functional Abdominal Pain Disorders in Children

To evaluate whether the use of pictograms improves symptom evaluation for children with functional abdominal pain disorders (FAPDs). This survey study was conducted in 2 academic centers and included patients aged 8-18years visiting the outpatient clinic for FAPD symptom evaluation. Patients were randomized to fill out the questionnaire without or with accompanying pictograms to assess gastrointestinal symptoms. Afterwards, patients underwent clinical health assessment by the healthcare professional (HCP). Subsequently, the HCP filled out the same questionnaire without pictograms, while blinded to the questionnaire completed by the patient. Primary outcome was the level of agreement between identified symptoms as assessed by patients and HCP. We included 144 children (questionnaire without accompanying pictograms [n=82] and with accompanying pictograms [n=62]). Overall agreements rates were not significantly different (without pictograms median, 70% vs with pictograms median, 70%). Accompanying pictograms did not significantly improve the assessment of abdominal pain symptoms. Accompanying pictograms were beneficial for concordance rates for nausea and vomiting symptoms (without pictograms median, 67% vs with pictograms median, 100%; P=.047). Subgroup analyses for children aged 8-12years of age revealed similar results (concordance on the presence of nausea and vomiting without pictograms median, 67% vs with pictograms median, 100%; P=.015). Subgroup analyses for children ages 12-18years showed no significant differences in concordance rates. Pictograms do not seem to improve the assessment of FAPDs. However, the use of pictograms improves the evaluation of nausea and vomiting, especially for children aged 8-12years. Therefore, HCPs could consider using pictograms in that setting during consultations.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk.

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.

Development and operationalisation of a mixed reality interactive virtual patient application for online nursing Objective Clinical Examinations

During the 2020 iteration of a Bachelor of Nursing Clinical Health Assessment skills course delivered in Singapore, the sudden cancellation of all face-to-face classes due to the COVID-19 pandemic resulted in innovative strategies being required and quickly created to enable students to successfully complete clinical skills laboratories and Objective Structured Clinical Examinations (OSCEs) online. However, the realism of the experience was rudimentary. At the end of the 2020 teaching semester, and the possibility that the next iteration of the course would also need to be online, it was decided to explore technologies to provide a more realistic and interactive user experience for the 2022 iteration of the Clinical Health Assessment skills course and particularly for the OSCEs. A research project was initiated in 2021, to develop and test the use of virtual or mixed reality applications for online simulated learning and clinical skills assessment. This paper discusses the development and operationalisation phases of a mixed reality interactive virtual patient application used for online OSCEs in a Clinical Health Assessment skills course.

A National Survey of Children, Adults, and the Elderly in the Fourth Wave of the COVID-19 Pandemic to Compare Acute and Post-COVID-19 Conditions in Saudi Arabia.

Background: The fight against COVID-19 appears to extend beyond screening and treatment of acute diseases to its medium- and long-term health consequences. Little is known about the epidemiology and the determinants of developing post-COVID-19 conditions (PCCs) among children. The aims of this study were to explore and determine the prevalence of PCCs among three age groups (children and adolescents, adults, and the elderly), and study the predictors of participants' return to their pre-COVID-19 health status among COVID-19 patients at least four weeks after they got sick, from February to 15 July 2022. Methods: This comparison survey study targeted 12,121 COVID-19 patients who fulfilled the selection criteria from the national register system and received a virtual assessment from the Medical Consultation Call Center (937), which was conducted by a well-trained family physician using a validated, well-structured assessment tool. The collected data were coded and analyzed using appropriate tests. Results: Out of the 12,121 recovered COVID-19 patients who received the virtual assessment calls, only 5909 (48.8%) agreed and completed the assessment. The majority of participants (4973, or 84.2%) reported no PCCs. The most common PCCs among young people were a cough, dyspnea, fatigue, and loss of appetite or weight loss, while among the elderly they were a cough, dyspnea, fatigue, stomachaches, poor concentration, sleep disturbance, and recurrent fever. Most post-COVID-19 cases require nothing more than reassurance and health education as only 384 (6.5%) required referral to primary health care centers (PHCCs.) The severity of COVID-19 infection, age group, sex, vaccination status, and body mass index were significant predictors for returning to the pre-infection health status and the required referral was significantly related to many factors. Conclusions: The comparison of children, adults, and the elderly with regard to the acute and post-COVID-19 conditions in Saudi Arabia in terms of the clinical health assessment and the required management plans showed significant differences.

Knowledge, attitude, and practice towards prevention of mother-to-child transmission of HIV among antenatal care attendees in Ethiopia, 2020.

In 2020, globally approximately 37.6 million people living with HIV and 700,000 children are born infected from their parents. Every day there are nearly 1800 new Human Immune Virus (HIV) infections in children, more than 90% occurring in the developing world. Approximately 90% of these infections are associated with mother-to-child transmission (MTCT). In 2019, Ethiopia had over 100,000 pregnancies in HIV-positive women and over 12,000 HIV-positive. Therefore, this study aimed to assess the knowledge, attitude, and practice of pregnant mothers toward the prevention of mother-to-child transmission of HIV.ss. An institutional-based cross-sectional study was conducted among 216 antenatal care (ANC) attendees in Gurage zone hospitals from June to July 2020. Data were collected using a structured and pre-tested questionnaire through face-to-face interviews. A Systematic random sampling technique with proportional allocation to size was used to select study subjects. Data entry and analysis were performed using Epi Data version 4.1 and SPSS version 25 respectively. The level of good knowledge, attitude, and practice towards Prevention of mother to child transmission (PMTCT) of HIV among antenatal care attendees was found to be 72.2%, 79%, and 62% respectively. This study has also shown that at the time of transmission of the virus from the infected mother to her child, 38.9% of the respondents responded that it could be through breastfeeding, 38.9% during pregnancy, 16.5% during labor and 5.7% did not know respectively. All the respondents have been tested and 0.92% was positive. Nearly half, 99 (45.8%) of the respondents had tested for HIV/AIDS with their partner/husband during their ANC follow-up. In this study, the level of good knowledge, attitude, and practice towards PMTCT of HIV among antenatal care attendees were low. This finding also suggests that healthcare providers should consider the potential risk of mother-to-child transmission of HIV while providing clinical health assessments during antenatal care visits. Thus, improvement of counseling sessions and knowledge of PMTCT for pregnant women attending antenatal care is needed to increase their acceptance and use of PMTCT for HIV services.

Years of play alter MRI measures of brain health in former Canadian Football League athletes: a pilot study

Introduction: Concussive and sub-concussive blows are commonly sustained during contact sports. Through a detailed neuroimaging analysis, this pilot study aimed to determine if a history of sport-related concussions exacerbated cognitive decline later in life. It was hypothesized that clinical health assessments and magnetic resonance imaging (MRI) techniques would provide insight into lasting health and well-being, structural, microstructural, and functional alterations caused by a history of concussive injuries. Materials and Methods: Twenty aging, retired Canadian Football League (rCFL) players (aged 56.9 ± 6.9) had clinical testing and MRI data acquired. Cortical thickness, voxel-wise diffusion tensor imaging (DTI), and Default Mode Network (DMN) connectivity data was collected for each subject and compared against healthy controls. Retired athlete age, playing position, and career length were also examined. Results: This study found widespread cortical thinning, significantly increased mean diffusivity, increased axial diffusivity, and both hyperactivity and hypoactivity within the DMN. Athlete age, position, and career length all influenced microstructural integrity. On average, retired athletes scored about 4 times greater depression-like and concussion-related symptoms and scored significantly lower in all health categories compared to healthy controls. Conclusions: These findings suggested that lasting signs of neurological injuries were present years after retiring from professional play.

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review.

Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

Accurate Detection of Doppler Cardiograms With a Parameterized Respiratory Filter Technique Using a K-Band Radar Sensor

Cardiogram is one of the most important factors for health assessment. As a new expression of cardiograms, the Doppler cardiograms (DCGs) detected remotely via Doppler radar sensor (DRS) provide rich details of the heart motion. However, the existing techniques for measurement of DCG requires that the subject holds his/her breath to avoid the disturbance caused by respiration. To realize the accurate detection of DCGs in the presence of respiration, a high-linearity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -band dc-coupled continuous-wave (CW) DRS with digital dc-tuning function was custom-designed and a novel parameterized respiratory filter (PRF) algorithm was proposed to precisely remove the respiratory signal. Both simulation and experimental results show that the proposed PRF algorithm has better DCG extraction performance than the conventional methods. Furthermore, the experimental results in normal and clinical environments show that the grained differential DCG (D-DCG) has promising potential in professional heart rate variability (HRV) analysis and heart diseases diagnosis, which means the proposed DCGs detection has the potential to be a convenient, comfortable, and reliable way for daily and clinical heart health assessment.

Variability in perceived burden and health trajectories among older caregivers: a population-based study in Sweden

BackgroundThe negative effects of informal caregiving are determined by the characteristics of the caregiver-care receiver dyad and the context of care. In this study, we aimed to identify which subgroups...

Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip.

Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose enormous complexity and challenges to rapid, facile, high-throughput, and accurate clinical analysis or health assessment. Deep-learning (DL)-assisted image analysis has been demonstrated to be an efficient big data process which shows accurate individual identification. However, the data-driven "black boxes" of current DL algorithms are suffering from the nontransparent inner working mechanism. In this work, we designed a programmable colorimetric chip with explainable DL to approach accurate classification and quantification analysis of sweat samples. Gel (sodium alginate) capsules with different indicators were adopted to combinate as designed programmable colorimetric chips. We collected 4600 colorimetric response images as the data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Glucose, pH, and lactate in human sweat could be facilely and 100% accurately classified and quantified by the convolutional neural network (CNN) DL algorithm, and the testing results of actual sweat via the DL-assisted colorimetric approach match 91.0-99.7% with the laboratory measurements. Class activation mapping (CAM) was processed to visualize the inner working mechanism of CNN operation, which could help to verify and explicate the design rationality of colorimetric chips. The explainable DL-assisted programmable colorimetric chip provided an "end-to-end" strategy to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for clinical monitoring, disease prevention, and even new scientific discoveries.