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Frontiers in Public Health 2024To explore the association between VPA and weight status in adolescents.
AIM
To explore the association between VPA and weight status in adolescents.
METHODS
The 2017/2018 Health Behavior in School-aged Children survey (HBSC) targeted children and adolescents aged 11, 13 and 15. A systematic multistage stratified cluster randomized sampling method was used in each participating country. The 2017/2018 survey enrolled over 240,951 adolescents across 45 countries and regions. Frequency of VPA, weight status and confounding factors were collected using a self-reported questionnaire.
RESULTS
Compared to daily VPA, less frequent VPA was linked to higher odds of obesity. For example, those who participating in VPA for 4-6 times a week (OR = 1.10, 95% CI = 1.06-1.13), 2-3 times a week (OR = 1.21, 95% CI = 1.17-1.25), or once a week (OR = 1.21, 95% CI = 1.16-1.25) all have higher odds of abnormal weight status. For boys, the frequency of 4-6 times a week (OR = 1.09, 95% CI = 1.04-1.13), 2-3 times a week (OR = 1.22, 95% CI = 1.17-1.27), or once a week (OR = 1.25, 95% CI = 1.19-1.32) were associated with higher odds of abnormal weight status. For girls, those who participating in VPA 4-6 times a week (OR = 1.11, 95% CI = 1.06-1.16), 2-3 times a week (OR = 1.20, 95% CI = 1.14-1.25), or once a week (OR = 1.17, 95% CI = 1.11-1.23) all have higher odds of abnormal weight status (i.e., overweight or obesity).
CONCLUSION
This population-based study suggests that infrequent VPA participation is associated with unhealthy weight status in adolescents compared to their physically active counterparts. Additionally, this association remains consistent in both boys and girls.
Topics: Humans; Male; Female; Cross-Sectional Studies; Exercise; Adolescent; Child; Body Weight; Surveys and Questionnaires; Pediatric Obesity; Self Report; Health Behavior
PubMed: 38932771
DOI: 10.3389/fpubh.2024.1402780 -
Viruses Jun 2024Human adenovirus-36 (HAdV-36) infection has been linked to obesity, low lipid levels, and improvements in blood glucose levels and insulin sensitivity in animal models... (Observational Study)
Observational Study
Human adenovirus-36 (HAdV-36) infection has been linked to obesity, low lipid levels, and improvements in blood glucose levels and insulin sensitivity in animal models and humans, although epidemiological studies remain controversial. Therefore, this study investigated the relationship between HAdV-36 seropositivity and glycemic control in youths. This observational study examined 460 youths (246 with normal weight and 214 obese subjects). All participants underwent assessments for anthropometry, blood pressure, circulating fasting levels of glucose, lipids, insulin, and anti-HAdV-36 antibodies; additionally, the homeostatic model assessment of insulin resistance (HOMA-IR) was calculated. In all, 57.17% of the subjects were HAdV-36 seropositive. Moreover, HAdV-36 seroprevalence was higher in obese subjects compared to their normal weight counterparts (59% vs. 55%). BMI (33.1 vs. 32.3 kg/m, = 0.03), and waist circumference (107 vs. 104 cm, = 0.02), insulin levels (21 vs. 16.3 µU/mL, = 0.003), and HOMA-IR (4.6 vs. 3.9, = 0.02) were higher in HAdV-36-positive subjects with obesity compared to seronegative subjects. In the obese group, HAdV-36 seropositivity was associated with a reducing effect in blood glucose levels in a model adjusted for total cholesterol, triglyceride levels, age and sex (β = -10.44, = 0.014). Furthermore, a statistically significant positive relationship was observed between HAdV-36 seropositivity and insulin levels in the obesity group. These findings suggest that natural HAdV-36 infection improves glycemic control but does not ameliorate hyperinsulinemia in obese subjects.
Topics: Humans; Male; Female; Blood Glucose; Insulin; Adolescent; Adenoviruses, Human; Obesity; Insulin Resistance; Adenovirus Infections, Human; Child; Seroepidemiologic Studies; Young Adult; Body Mass Index; Antibodies, Viral
PubMed: 38932214
DOI: 10.3390/v16060922 -
Polymers Jun 2024Additive manufacturing (AM) has arisen as a transformative technology for manufacturing complex geometries with enhanced mechanical properties, particularly in the realm... (Review)
Review
Additive manufacturing (AM) has arisen as a transformative technology for manufacturing complex geometries with enhanced mechanical properties, particularly in the realm of continuous fiber-reinforced polymer composites (CFRPCs). Among various AM techniques, fused deposition modeling (FDM) stands out as a promising method for the fabrication of CFRPCs due to its versatility, ease of use, flexibility, and cost-effectiveness. Several research papers on the AM of CFRPs via FDM were summarized and therefore this review paper provides a critical examination of the process-printing parameters influencing the AM process, with a focus on their impact on mechanical properties. This review covers details of factors such as fiber orientation, layer thickness, nozzle diameter, fiber volume fraction, printing temperature, and infill design, extracted from the existing literature. Through a visual representation of the process parameters (printing and material) and properties (mechanical, physical, and thermal), this paper aims to separate out the optimal processing parameters that have been inferred from various research studies. Furthermore, this analysis critically evaluates the current state-of-the-art research, highlighting advancements, applications, filament production methods, challenges, and opportunities for further development in this field. In comparison to short fibers, continuous fiber filaments can render better strength; however, delamination issues persist. Various parameters affect the printing process differently, resulting in several limitations that need to be addressed. Signifying the relationship between printing parameters and mechanical properties is vital for optimizing CFRPC fabrication via FDM, enabling the realization of lightweight, high-strength components for various industrial applications.
PubMed: 38931971
DOI: 10.3390/polym16121622 -
Pharmaceutics Jun 2024Cancer represents a significant threat to human health. The cells and tissues within the microenvironment of solid tumors exhibit complex and abnormal properties in... (Review)
Review
Cancer represents a significant threat to human health. The cells and tissues within the microenvironment of solid tumors exhibit complex and abnormal properties in comparison to healthy tissues. The efficacy of nanomedicines is inhibited by the presence of substantial and complex physical barriers in the tumor tissue. The latest generation of intelligent drug delivery systems, particularly nanomedicines capable of charge reversal, have shown promise in addressing this issue. These systems can transform their charge from negative to positive upon reaching the tumor site, thereby enhancing tumor penetration via transcytosis and promoting cell internalization by interacting with the negatively charged cell membranes. The modification of nanocarriers with 2,3-dimethylmaleic anhydride (DMMA) and its derivatives, which are responsive to weak acid stimulation, represents a significant advance in the field of charge-reversal nanomedicines. This review provides a comprehensive examination of the recent insights into DMMA-modified nanocarriers in drug delivery systems, with a particular focus on their potential in targeted therapeutics. It also discusses the synthesis of DMMA derivatives and their role in charge reversal, shell detachment, size shift, and ligand reactivation mechanisms, offering the prospect of a tailored, next-generation therapeutic approach to overcome the diverse challenges associated with cancer therapy.
PubMed: 38931929
DOI: 10.3390/pharmaceutics16060809 -
Sensors (Basel, Switzerland) Jun 2024Heart rate variability (HRV) is related to cardiac vagal control and emotional regulation and an index for cardiac vagal control and cardiac autonomic activity. This...
Heart rate variability (HRV) is related to cardiac vagal control and emotional regulation and an index for cardiac vagal control and cardiac autonomic activity. This study aimed to develop the Taiwan HRV normative database covering individuals aged 20 to 70 years and to assess its diagnosing validity in patients with major depressive disorder (MDD). A total of 311 healthy participants were in the HRV normative database and divided into five groups in 10-year age groups, and then the means and standard deviations of the HRV indices were calculated. We recruited 272 patients with MDD for cross-validation, compared their HRV indices with the normative database, and then converted them to Z-scores to explore the deviation of HRV in MDD patients from healthy groups. The results found a gradual decline in HRV indices with advancing age in the HC group, and females in the HC group exhibit higher cardiac vagal control and parasympathetic activity than males. Conversely, patients in the MDD group demonstrate lower HRV indices than those in the HC group, with their symptoms of depression and anxiety showing a negative correlation with HRV indices. The Taiwan HRV normative database has good psychometric characteristics of cross-validation.
Topics: Humans; Heart Rate; Depressive Disorder, Major; Male; Female; Adult; Middle Aged; Aged; Autonomic Nervous System; Young Adult; Databases, Factual; Taiwan; Electrocardiography; Heart
PubMed: 38931788
DOI: 10.3390/s24124003 -
Sensors (Basel, Switzerland) Jun 2024Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical...
Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.
Topics: Humans; Respiratory Rate; Neural Networks, Computer; Photoplethysmography; Signal Processing, Computer-Assisted; Heart Rate; Algorithms; Deep Learning
PubMed: 38931763
DOI: 10.3390/s24123980 -
Sensors (Basel, Switzerland) Jun 2024The development of non-contact techniques for monitoring human vital signs has significant potential to improve patient care in diverse settings. By facilitating easier... (Review)
Review
The development of non-contact techniques for monitoring human vital signs has significant potential to improve patient care in diverse settings. By facilitating easier and more convenient monitoring, these techniques can prevent serious health issues and improve patient outcomes, especially for those unable or unwilling to travel to traditional healthcare environments. This systematic review examines recent advancements in non-contact vital sign monitoring techniques, evaluating publicly available datasets and signal preprocessing methods. Additionally, we identified potential future research directions in this rapidly evolving field.
Topics: Humans; Vital Signs; Monitoring, Physiologic; Signal Processing, Computer-Assisted
PubMed: 38931747
DOI: 10.3390/s24123963 -
Sensors (Basel, Switzerland) Jun 2024Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait... (Meta-Analysis)
Meta-Analysis Review
Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
Topics: Humans; Parkinson Disease; Machine Learning; Gait Disorders, Neurologic; Gait; Wearable Electronic Devices; Algorithms; Quality of Life
PubMed: 38931743
DOI: 10.3390/s24123959 -
Sensors (Basel, Switzerland) Jun 2024Midlife risk factors such as type 2 diabetes mellitus (T2DM) confer a significantly increased risk of cognitive impairment in later life with executive function, memory,...
Midlife risk factors such as type 2 diabetes mellitus (T2DM) confer a significantly increased risk of cognitive impairment in later life with executive function, memory, and attention domains often affected first. Spatiotemporal gait characteristics are emerging as important integrative biomarkers of neurocognitive function and of later dementia risk. We examined 24 spatiotemporal gait parameters across five domains of gait previously linked to cognitive function on usual-pace, maximal-pace, and cognitive dual-task gait conditions in 102 middle-aged adults with (57.5 ± 8.0 years; 40% female) and without (57.0 ± 8.3 years; 62.1% female) T2DM. Neurocognitive function was measured using a neuropsychological assessment battery. T2DM was associated with significant changes in gait phases and rhythm domains at usual pace, and greater gait variability observed during maximal pace and dual tasks. In the overall cohort, both the gait pace and rhythm domains were associated with memory and executive function during usual pace. At maximal pace, gait pace parameters were associated with reaction time and delayed memory. During the cognitive dual task, associations between gait variability and both delayed memory/executive function were observed. Associations persisted following covariate adjustment and did not differ by T2DM status. Principal components analysis identified a consistent association of slower gait pace (step/stride length) and increased gait variability during maximal-pace walking with poorer memory and executive function performance. These data support the use of spatiotemporal gait as an integrative biomarker of neurocognitive function in otherwise healthy middle-aged individuals and reveal discrete associations between both differing gait tasks and gait domains with domain-specific neuropsychological performance. Employing both maximal-pace and dual-task paradigms may be important in cognitively unimpaired populations with risk factors for later cognitive decline-with the aim of identifying individuals who may benefit from potential preventative interventions.
Topics: Humans; Female; Middle Aged; Male; Gait; Diabetes Mellitus, Type 2; Neuropsychological Tests; Executive Function; Cognition; Memory; Aged
PubMed: 38931687
DOI: 10.3390/s24123903 -
Sensors (Basel, Switzerland) Jun 2024Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users...
Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.
Topics: Photoplethysmography; Heart Rate; Humans; Signal Processing, Computer-Assisted; Algorithms; Monitoring, Physiologic; Wearable Electronic Devices
PubMed: 38931686
DOI: 10.3390/s24123901