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Sports Medicine (Auckland, N.Z.) Nov 2023The primary aim of our systematic review and meta-analysis was to investigate the effect of resistance training on academic outcomes in school-aged youth.
BACKGROUND
The primary aim of our systematic review and meta-analysis was to investigate the effect of resistance training on academic outcomes in school-aged youth.
METHODS
We conducted a systematic search of six electronic databases (CINAHL Complete, PsycINFO, SCOPUS, Ovid MEDLINE, SPORTDiscus and EMBASE) with no date restrictions. Studies were eligible if they: (a) included school-aged youth (5-18 years), and (b) examined the effect of resistance training on academic outcomes (i.e., cognitive function, academic achievement, and/or on-task behaviour in the classroom). Risk of bias was assessed using the appropriate Cochrane Risk of Bias Tools, funnel plots and Egger's regression asymmetry tests. A structural equation modelling approach was used to conduct the meta-analysis.
RESULTS
Fifty-three studies were included in our systematic review. Participation in resistance training (ten studies with 53 effect sizes) had a small positive effect on the overall cognitive, academic and on-task behaviours in school-aged youth (standardized mean difference (SMD) 0.19, 95% confidence interval (CI) 0.05-0.32). Resistance training was more effective (SMD 0.26, 95% CI 0.10-0.42) than concurrent training, i.e., the combination of resistance training and aerobic training (SMD 0.11, 95% CI - 0.05-0.28). An additional 43 studies (including 211 effect sizes) examined the association between muscular fitness and cognition or academic achievement, also yielding a positive relationship (SMD 0.13, 95% CI 0.10-0.16).
CONCLUSION
This review provides preliminary evidence that resistance training may improve cognitive function, academic performance, and on-task behaviours in school-aged youth.
PROSPERO REGISTRATION
CRD42020175695.
PubMed: 37466900
DOI: 10.1007/s40279-023-01881-6 -
JAMA Network Open Dec 2023There is a gap in the evidence regarding nature-based interventions (NBIs) for children with autism spectrum disorder (ASD). (Meta-Analysis)
Meta-Analysis
IMPORTANCE
There is a gap in the evidence regarding nature-based interventions (NBIs) for children with autism spectrum disorder (ASD).
OBJECTIVE
To systematically review and meta-analyze available evidence on the health-related outcomes in NBIs for children with ASD.
DATA SOURCES
The Cumulative Index to Nursing and Allied Health Literature, Cochrane, Embase, Emcare, Education Resources Information Center, Global Health, MEDLINE, PsycInfo, SPORTDiscus, and Web of Science were searched from inception until May 2023. Google Scholar and references from included studies were searched for additional studies.
STUDY SELECTION
Included studies were randomized clinical trials (RCTs), controlled studies, and single-group before-and-after studies that reported health-related outcomes.
DATA EXTRACTION AND SYNTHESIS
This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) reporting guidelines. Random-effects meta-analyses were used to synthesize the data. The findings of studies that were ineligible for meta-analysis were summarized according to the Synthesis Without Meta-analysis (SWIM) reporting guidelines.
MAIN OUTCOMES AND MEASURES
The outcomes of interest were health-related outcomes (ie, social functioning, behavioral functioning, emotional functioning, sensory functioning) and the self-reported well-being of children with ASD.
RESULTS
A total of 24 studies with 717 participants (mean age range, 5.3 to 17.8 years; 141 [21.9%] female) were included. A meta-analysis from 13 studies indicated a significant negative moderate association between NBIs and social communication (standardized mean difference [SMD], -0.59; 95% CI, -0.85 to -0.34). For behavioral functioning outcomes, NBIs showed a significant moderate association with reduced hyperactivity (SMD, -0.56; 95% CI, -0.86 to -0.26) and a small to moderate association with reduced irritability (SMD, -0.49; 95% CI, -0.79 to -0.19). For sensory functioning, NBIs were significantly associated with improved inattention and distractibility (SMD, 1.13; 95% CI, 0.67 to 1.60). Significant moderate associations were observed in sensory seeking (SMD, 0.77; 95% CI, 0.33 to 1.22; P < .001; I2 = 0%) and sensory sensitivity (SMD, 0.56; 95% CI, 0.12 to 1.00; P = .01; I2 = 0%). Heterogeneity of the intervention effects was not high, and I2 ranged from 0% to 67%.
CONCLUSIONS AND RELEVANCE
The findings of this systematic review and meta-analysis suggested an association of NBIs in group-based recreational therapy with experiential learning with positive short-term outcomes on sensory, social, and behavioral functioning for children with ASD. Future evidence using robust study design to aid the health and functional trajectories of children with ASD is recommended.
Topics: Child; Female; Humans; Child, Preschool; Adolescent; Male; Autistic Disorder; Emotions; Autism Spectrum Disorder; Behavior Therapy; Communication
PubMed: 38060224
DOI: 10.1001/jamanetworkopen.2023.46715 -
NPJ Digital Medicine Sep 2023Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images... (Review)
Review
Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86-98; n = 11), rosacea (94%, 90-97; n = 4), eczema (93%, 90-99; n = 9) and psoriasis (89%, 78-92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93-100%, n = 2), eczema (88%, n = 1), and acne (67-86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.
PubMed: 37758829
DOI: 10.1038/s41746-023-00914-8 -
Nurse Education in Practice Nov 2023The aim of this scoping review is to summarize and critically evaluate research focused on nursing bridging education programs internationally. Specifically, this review... (Review)
Review
AIM
The aim of this scoping review is to summarize and critically evaluate research focused on nursing bridging education programs internationally. Specifically, this review addresses bridging from a: (1) Personal Support Worker (or similar) to a Registered Practical Nurse (or similar); and (2) Registered Practical Nurse (or similar) to a Registered Nurse.
BACKGROUND
Nursing bridging education programs support learners to move from one level of educational preparation or practice to another. These programs can therefore increase nursing workforce capacity. Global healthcare systems have faced nursing shortages for decades. Moreover, the presently insufficient nursing workforce is confronting an ever-increasing volume of needed healthcare that is rising with the global ageing demographic shift.
DESIGN
The Joanna Briggs Institute methods for scoping reviews, combined with Arksey and O'Malley's (2005) guidelines, were used with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR).
METHODS
MEDLINE (Ovid), CINAHL, EMBASE and SCOPUS databases were searched. Articles published in English that included Personal Support Workers, Registered Practical Nurses, Registered Nurses and/or nurses in similar categories who were studied through the process of a nursing bridging education program were included in the review. The study search was limited to papers published after 2005 (i.e., the beginning of nurse workload "overload" according to the Canadian Nurses Association). Braun and Clarke's (2006) thematic analysis was used in a content analysis of the included studies.
RESULTS
A total of 15 articles published between 2005 and 2022 were included. Four themes were generated: (1) participating in bridging education programs fuels both professional and personal development; (2) nursing bridging education programs enhance diversity in the nursing workforce; (3) student nurses do not anticipate the challenges associated with participating in a bridging program; and (4) mentor-mentee connection promotes academic learning and successful completion of nursing bridging education programs.
CONCLUSIONS
Despite experiencing challenges, participation in/completion of nursing bridging education programs leads to successful role transitioning and self-reported fulfillment of personal and professional aspirations. This review revealed the need for bridging programs to accommodate the unique needs of student nurses. Incorporation of support services, mentorship and faculty familiarity with varying nursing educational backgrounds facilitates role transitions by reducing the perceived challenges of bridging and promoting connection to foster learning. Nursing bridging education programs allow greater numbers of nurses to be trained to build workforce capacity and enable care for the world's rapidly ageing population.
Topics: Humans; Canada; Education, Nursing; Learning; Nursing Staff; Delivery of Health Care
PubMed: 37952474
DOI: 10.1016/j.nepr.2023.103833 -
PloS One 2023Whether food source or energy mediates the effect of fructose-containing sugars on blood pressure (BP) is unclear. We conducted a systematic review and meta-analysis of... (Meta-Analysis)
Meta-Analysis
Whether food source or energy mediates the effect of fructose-containing sugars on blood pressure (BP) is unclear. We conducted a systematic review and meta-analysis of the effect of different food sources of fructose-containing sugars at different levels of energy control on BP. We searched MEDLINE, Embase and the Cochrane Library through June 2021 for controlled trials ≥7-days. We prespecified 4 trial designs: substitution (energy matched substitution of sugars); addition (excess energy from sugars added); subtraction (excess energy from sugars subtracted); and ad libitum (energy from sugars freely replaced). Outcomes were systolic and diastolic BP. Independent reviewers extracted data. GRADE assessed the certainty of evidence. We included 93 reports (147 trial comparisons, N = 5,213) assessing 12 different food sources across 4 energy control levels in adults with and without hypertension or at risk for hypertension. Total fructose-containing sugars had no effect in substitution, subtraction, or ad libitum trials but decreased systolic and diastolic BP in addition trials (P<0.05). There was evidence of interaction/influence by food source: fruit and 100% fruit juice decreased and mixed sources (with sugar-sweetened beverages [SSBs]) increased BP in addition trials and the removal of SSBs (linear dose response gradient) and mixed sources (with SSBs) decreased BP in subtraction trials. The certainty of evidence was generally moderate. Food source and energy control appear to mediate the effect of fructose-containing sugars on BP. The evidence provides a good indication that fruit and 100% fruit juice at low doses (up to or less than the public health threshold of ~10% E) lead to small, but important reductions in BP, while the addition of excess energy of mixed sources (with SSBs) at high doses (up to 23%) leads to moderate increases and their removal or the removal of SSBs alone (up to ~20% E) leads to small, but important decreases in BP in adults with and without hypertension or at risk for hypertension. Trial registration: Clinicaltrials.gov: NCT02716870.
Topics: Adult; Humans; Fructose; Blood Pressure; Fruit; Sugars; Hypertension
PubMed: 37582096
DOI: 10.1371/journal.pone.0264802 -
Frontiers in Neurology 2023An association between weight status and migraine has been previously reported; however, this relationship has only been studied in adults, not in the paediatric...
INTRODUCTION
An association between weight status and migraine has been previously reported; however, this relationship has only been studied in adults, not in the paediatric population.
OBJECTIVE
To evaluate the association between weight status and migraine in the paediatric population.
METHODS
We searched PubMed/Medline, Scopus, Web of Science, Ovid Medline, and Embase using a cut-off date of May 2023. We included observational studies that evaluated the association between weight status (underweight, overweight, obese, and excess weight) and migraine in the paediatric population (children and adolescents). Normal weight was the comparator. The outcome was migraine (all types, episodic and chronic). We performed meta-analyses using a random-effects model to estimate the pooled effects for each outcome. Sensitivity analysis was performed based on study design and risk of bias (using the Newcastle-Ottawa Scale). Certainty of evidence was assessed using the GRADE approach.
RESULTS
Eight studies (6 cross-sectional, 1 case-control and 1 cohort) covering 16,556 patients were included. The overall certainty of evidence was very low for the association between overweight, obesity, and excess weight with migraine. In the sensitivity analysis, meta-analyses of studies with a low risk of bias found that the overweight population probably had an increased odds of migraine (OR: 1.70; 95% CI: 1.14 to 2.53; = 32.3%, = 0.224) and that excess weight may increase the odds of migraine (OR: 1.58; 95% CI: 1.06 to 2.35; = 83.7%, = 0.002). Additionally, cohort and case-control studies found that obesity probably increases the odds of migraine. No studies analysed the association between underweight and migraine.
CONCLUSION
The associations between overweight, obesity, excess weight and migraine were uncertain, but studies with better methodological quality reported increased odds. Future longitudinal studies with proper confounding control are needed to disentangle their causal relationship.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO, identifier CRD42021271533.
PubMed: 38033769
DOI: 10.3389/fneur.2023.1225935 -
Therapeutic Advances in Ophthalmology 2024New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural... (Review)
Review
BACKGROUND
New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way.
OBJECTIVES
This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies.
DESIGN
A multidimensional comprehensive systematic narrative review.
DATA SOURCES AND METHODS
A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed.
RESULTS
Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes.
CONCLUSION
The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients.
TRIAL REGISTRATION
The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.
PubMed: 38516169
DOI: 10.1177/25158414241232258 -
JAMA Psychiatry Aug 2023Social anxiety disorder (SAD) can be adequately treated with cognitive behavioral therapy (CBT). However, there is a large gap in knowledge on factors associated with... (Meta-Analysis)
Meta-Analysis
Baseline Severity as a Moderator of the Waiting List-Controlled Association of Cognitive Behavioral Therapy With Symptom Change in Social Anxiety Disorder: A Systematic Review and Individual Patient Data Meta-analysis.
IMPORTANCE
Social anxiety disorder (SAD) can be adequately treated with cognitive behavioral therapy (CBT). However, there is a large gap in knowledge on factors associated with prognosis, and it is unclear whether symptom severity predicts response to CBT for SAD.
OBJECTIVE
To examine baseline SAD symptom severity as a moderator of the association between CBT and symptom change in patients with SAD.
DATA SOURCES
For this systematic review and individual patient data meta-analysis (IPDMA), PubMed, PsycInfo, Embase, and the Cochrane Library were searched from January 1, 1990, to January 13, 2023. Primary search topics were social anxiety disorder, cognitive behavior therapy, and randomized controlled trial.
STUDY SELECTION
Inclusion criteria were randomized clinical trials comparing CBT with being on a waiting list and using the Liebowitz Social Anxiety Scale (LSAS) in adults with a primary clinical diagnosis of SAD.
DATA EXTRACTION AND SYNTHESIS
Authors of included studies were approached to provide individual-level data. Data were extracted by pairs of authors following the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline, and risk of bias was assessed using the Cochrane tool. An IPDMA was conducted using a 2-stage approach for the association of CBT with change in LSAS scores from baseline to posttreatment and for the interaction effect of baseline LSAS score by condition using random-effects models.
MAIN OUTCOMES AND MEASURES
The main outcome was the baseline to posttreatment change in symptom severity measured by the LSAS.
RESULTS
A total of 12 studies including 1246 patients with SAD (mean [SD] age, 35.3 [10.9] years; 738 [59.2%] female) were included in the meta-analysis. A waiting list-controlled association between CBT and pretreatment to posttreatment LSAS change was found (b = -20.3; 95% CI, -24.9 to -15.6; P < .001; Cohen d = -0.95; 95% CI, -1.16 to -0.73). Baseline LSAS scores moderated the differences between CBT and waiting list with respect to pretreatment to posttreatment symptom reductions (b = -0.22; 95% CI, -0.39 to -0.06; P = .009), indicating that individuals with severe symptoms had larger waiting list-controlled symptom reductions after CBT (Cohen d = -1.13 [95% CI, -1.39 to -0.88] for patients with very severe SAD; Cohen d = -0.54 [95% CI, -0.80 to -0.29] for patients with mild SAD).
CONCLUSIONS AND RELEVANCE
In this systematic review and IPDMA, higher baseline SAD symptom severity was associated with greater (absolute but not relative) symptom reductions after CBT in patients with SAD. The findings contribute to personalized care by suggesting that clinicians can confidently offer CBT to individuals with severe SAD symptoms.
Topics: Adult; Humans; Female; Male; Phobia, Social; Waiting Lists; Cognitive Behavioral Therapy; Randomized Controlled Trials as Topic
PubMed: 37256597
DOI: 10.1001/jamapsychiatry.2023.1291 -
Journal of Imaging Dec 2023Three-dimensional human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive... (Review)
Review
Three-dimensional human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject. For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information. Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses. Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation.
PubMed: 38132693
DOI: 10.3390/jimaging9120275 -
International Journal of Medical... Sep 2023Natural Language Processing (NLP) applications have developed over the past years in various fields including its application to clinical free text for named entity... (Review)
Review
BACKGROUND
Natural Language Processing (NLP) applications have developed over the past years in various fields including its application to clinical free text for named entity recognition and relation extraction. However, there has been rapid developments the last few years that there's currently no overview of it. Moreover, it is unclear how these models and tools have been translated into clinical practice. We aim to synthesize and review these developments.
METHODS
We reviewed literature from 2010 to date, searching PubMed, Scopus, the Association of Computational Linguistics (ACL), and Association of Computer Machinery (ACM) libraries for studies of NLP systems performing general-purpose (i.e., not disease- or treatment-specific) information extraction and relation extraction tasks in unstructured clinical text (e.g., discharge summaries).
RESULTS
We included in the review 94 studies with 30 studies published in the last three years. Machine learning methods were used in 68 studies, rule-based in 5 studies, and both in 22 studies. 63 studies focused on Named Entity Recognition, 13 on Relation Extraction and 18 performed both. The most frequently extracted entities were "problem", "test" and "treatment". 72 studies used public datasets and 22 studies used proprietary datasets alone. Only 14 studies defined clearly a clinical or information task to be addressed by the system and just three studies reported its use outside the experimental setting. Only 7 studies shared a pre-trained model and only 8 an available software tool.
DISCUSSION
Machine learning-based methods have dominated the NLP field on information extraction tasks. More recently, Transformer-based language models are taking the lead and showing the strongest performance. However, these developments are mostly based on a few datasets and generic annotations, with very few real-world use cases. This may raise questions about the generalizability of findings, translation into practice and highlights the need for robust clinical evaluation.
Topics: Humans; Natural Language Processing; Machine Learning; Language; Information Storage and Retrieval; PubMed
PubMed: 37295138
DOI: 10.1016/j.ijmedinf.2023.105122