%0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64539 %T Effect of Physical Exercise on Telomere Length: Umbrella Review and Meta-Analysis %A Sánchez-González,Juan Luis %A Sánchez-Rodríguez,Juan Luis %A González-Sarmiento,Rogelio %A Navarro-López,Víctor %A Juárez-Vela,Raúl %A Pérez,Jesús %A Martín-Vallejo,Javier %K aging %K chromosome %K exercise %K meta-analysis %K telomere %K telomerase %K genes %K genome %K DNA %D 2025 %7 10.1.2025 %9 %J JMIR Aging %G English %X Background: Telomere length (TL) is a marker of cellular health and aging. Physical exercise has been associated with longer telomeres and, therefore, healthier aging. However, results supporting such effects vary across studies. Our aim was to synthesize existing evidence on the effect of different modalities and durations of physical exercise on TL. Objective: The aim of this study was to explore the needs and expectations of individuals with physical disabilities and their interventionists for the use of a virtual reality physical activity platform in a community organization. Methods: We performed an umbrella review and meta-analysis. Data sources included PubMed, Embase, Web of Science, Cochrane Library, and Scopus. We selected systematic reviews and meta-analyses of randomized and nonrandomized controlled clinical trials evaluating the effect of physical exercise on TL. Results: Our literature search retrieved 12 eligible systematic reviews, 5 of which included meta-analyses. We identified 22 distinct primary studies to estimate the overall effect size of physical exercise on TL. The overall effect size was 0.28 (95% CI 0.118-0.439), with a heterogeneity test value Q of 43.08 (P=.003) and I² coefficient of 51%. The number of weeks of intervention explained part of this heterogeneity (Q_B=8.25; P=.004), with higher effect sizes found in studies with an intervention of less than 30 weeks. Exercise modality explained additional heterogeneity within this subgroup (Q_B=10.28, P=.02). The effect sizes were small for aerobic exercise and endurance training, and moderate for high-intensity interval training. Conclusions: Our umbrella review and meta-analysis detected a small-moderate positive effect of physical exercise on TL, which seems to be influenced by the duration and type of physical exercise. High quality studies looking into the impact of standardized, evidence-based physical exercise programs on TL are still warranted. Trial Registration: PROSPERO CRD42024500736; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=500736 %R 10.2196/64539 %U https://aging.jmir.org/2025/1/e64539 %U https://doi.org/10.2196/64539 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 5 %N %P e58439 %T Internet-Based Abnormal Chromosomal Diagnosis During Pregnancy Using a Noninvasive Innovative Approach to Detecting Chromosomal Abnormalities in the Fetus: Scoping Review %A Oyovwi,Mega Obukohwo Sr %A Ohwin,Ejiro Peggy %A Rotu,Rume Arientare %A Olowe,Temitope Gideon %+ Department of Physiology, Adeleke University, Ede, Osun State, Ede, 33105, Nigeria, 234 8066096369, megalect@gmail.com %K internet-based %K abnormal chromosomal diagnosis %K pregnancy %K noninvasive %K innovative approach %K detecting %K preventing %K chromosomal abnormalities %K fetus %D 2024 %7 16.10.2024 %9 Review %J JMIR Bioinform Biotech %G English %X Background: Chromosomal abnormalities are genetic disorders caused by chromosome errors, leading to developmental delays, birth defects, and miscarriages. Currently, invasive procedures such as amniocentesis or chorionic villus sampling are mostly used, which carry a risk of miscarriage. This has led to the need for a noninvasive and innovative approach to detect and prevent chromosomal abnormalities during pregnancy. Objective: This review aims to describe and appraise the potential of internet-based abnormal chromosomal preventive measures as a noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy. Methods: A thorough review of existing literature and research on chromosomal abnormalities and noninvasive approaches to prenatal diagnosis and therapy was conducted. Electronic databases such as PubMed, Google Scholar, ScienceDirect, CENTRAL, CINAHL, Embase, OVID MEDLINE, OVID PsycINFO, Scopus, ACM, and IEEE Xplore were searched for relevant studies and articles published in the last 5 years. The keywords used included chromosomal abnormalities, prenatal diagnosis, noninvasive, and internet-based, and diagnosis. Results: The review of literature revealed that internet-based abnormal chromosomal diagnosis is a potential noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy. This innovative approach involves the use of advanced technology, including high-resolution ultrasound, cell-free DNA testing, and bioinformatics, to analyze fetal DNA from maternal blood samples. It allows early detection of chromosomal abnormalities, enabling timely interventions and treatment to prevent adverse outcomes. Furthermore, with the advancement of technology, internet-based abnormal chromosomal diagnosis has emerged as a safe alternative with benefits including its cost-effectiveness, increased accessibility and convenience, potential for earlier detection and intervention, and ethical considerations. Conclusions: Internet-based abnormal chromosomal diagnosis has the potential to revolutionize prenatal care by offering a safe and noninvasive alternative to invasive procedures. It has the potential to improve the detection of chromosomal abnormalities, leading to better pregnancy outcomes and reduced risk of miscarriage. Further research and development in this field is needed to make this approach more accessible and affordable for pregnant women. %M 39412876 %R 10.2196/58439 %U https://bioinform.jmir.org/2024/1/e58439 %U https://doi.org/10.2196/58439 %U http://www.ncbi.nlm.nih.gov/pubmed/39412876 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 3 %N 1 %P e40473 %T Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review %A Cardozo,Glauco %A Tirloni,Salvador Francisco %A Pereira Moro,Antônio Renato %A Marques,Jefferson Luiz Brum %+ Federal Institute of Santa Catarina, Av. Mauro Ramos, 950 - Centro, Florianópolis, 88020-300, Brazil, 55 48984060740, glauco.cardozo@ifsc.edu.br %K review %K laboratory tests %K machine learning %K prediction %K diagnosis %K COVID-19 %D 2022 %7 23.12.2022 %9 Review %J JMIR Bioinform Biotech %G English %X Background: In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques. Objective: In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases. Methods: The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement. Results: Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count. Conclusions: Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases. %M 36644762 %R 10.2196/40473 %U https://bioinform.jmir.org/2022/1/e40473 %U https://doi.org/10.2196/40473 %U http://www.ncbi.nlm.nih.gov/pubmed/36644762 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 3 %N 1 %P e36791 %T The Utilization of Heart Rate Variability for Autonomic Nervous System Assessment in Healthy Pregnant Women: Systematic Review %A Sharifiheris,Zahra %A Rahmani,Amir %A Onwuka,Joseph %A Bender,Miriam %+ University of California, Irvine, 8420 Palo Verde, Irvine, CA, 92697, United States, 1 6506805432, sharifiz@uci.edu %K heart rate variability %K pregnancy %K systematic review %K autonomic nervous system assessment %D 2022 %7 17.11.2022 %9 Review %J JMIR Bioinform Biotech %G English %X Background: The autonomic nervous system (ANS) plays a central role in pregnancy-induced adaptations, and failure in the required adaptations is associated with adverse neonatal and maternal outcomes. Mapping maternal ANS function in healthy pregnancy may help to understand ANS function. Objective: This study aimed to systematically review studies on the use of heart rate variability (HRV) monitoring to measure ANS function during pregnancy and determine whether specific HRV patterns representing normal ANS function have been identified during pregnancy. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was used to guide the systematic review. The CINAHL, PubMed, SCOPUS, and Web of Science databases were searched to comprehensively identify articles without a time span limitation. Studies were included if they assessed HRV in healthy pregnant individuals at least once during pregnancy or labor, with or without a comparison group (eg, complicated pregnancy). Quality assessment of the included literature was performed using the National Heart, Lung, and Blood Institute (NHLBI) tool. A narrative synthesis approach was used for data extraction and analysis, as the articles were heterogenous in scope, approaches, methods, and variables assessed, which precluded traditional meta-analysis approaches being used. Results: After full screening, 8 studies met the inclusion criteria. In 88% (7/8) of the studies, HRV was measured using electrocardiogram and operationalized in 3 different ways: linear frequency domain (FD), linear time domain (TD), and nonlinear methods. FD was measured in all (8/8), TD in 75% (6/8), and nonlinear methods in 25% (2/8) of the studies. The assessment duration varied from 5 minutes to 24 hours. TD indexes and most of the FD indexes decreased from the first to the third trimesters in the majority (5/7, 71%) of the studies. Of the FD indexes, low frequency (LF [nu]) and the LF/high frequency (HF) ratio showed an ascending trend from early to late pregnancy, indicating an increase in sympathetic activity toward the end of the pregnancy. Conclusions: We identified 3 HRV operationalization methods along with potentially indicative HRV patterns. However, we found no justification for the selection of measurement tools, measurement time frames, and operationalization methods, which threaten the generalizability and reliability of pattern findings. More research is needed to determine the criteria and methods for determining HRV patterns corresponding to ANS functioning in healthy pregnant persons. %R 10.2196/36791 %U https://bioinform.jmir.org/2022/1/e36791 %U https://doi.org/10.2196/36791 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 3 %N 1 %P e39618 %T Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review %A Dlima,Schenelle Dayna %A Shevade,Santosh %A Menezes,Sonia Rebecca %A Ganju,Aakash %+ Saathealth, 1103, Glen Croft, Hiranandani Gardens, Powai, Mumbai, 400076, India, 971 559558006, schenelle@saathealth.com %K digital phenotyping %K machine learning %K personal device data %K passive data %K active data %K wearable device %K wearable sensor %K mobile application %K digital health %D 2022 %7 18.7.2022 %9 Review %J JMIR Bioinform Biotech %G English %X Background: Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured. Objective: The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications. Methods: We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations. Results: A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common. Conclusions: Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build “digital phenotypes” to personalize digital health interventions and treatment plans. %R 10.2196/39618 %U https://bioinform.jmir.org/2022/1/e39618 %U https://doi.org/10.2196/39618