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NPJ Digital Medicine Mar 2021The ubiquitous and openly accessible information produced by the public on the Internet has sparked an increasing interest in developing digital public health... (Review)
Review
The ubiquitous and openly accessible information produced by the public on the Internet has sparked an increasing interest in developing digital public health surveillance (DPHS) systems. We conducted a systematic scoping review in accordance with the PRISMA extension for scoping reviews to consolidate and characterize the existing research on DPHS and identify areas for further research. We used Natural Language Processing and content analysis to define the search strings and searched Global Health, Web of Science, PubMed, and Google Scholar from 2005 to January 2020 for peer-reviewed articles on DPHS, with extensive hand searching. Seven hundred fifty-five articles were included in this review. The studies were from 54 countries and utilized 26 digital platforms to study 208 sub-categories of 49 categories associated with 16 public health surveillance (PHS) themes. Most studies were conducted by researchers from the United States (56%, 426) and dominated by communicable diseases-related topics (25%, 187), followed by behavioural risk factors (17%, 131). While this review discusses the potentials of using Internet-based data as an affordable and instantaneous resource for DPHS, it highlights the paucity of longitudinal studies and the methodological and inherent practical limitations underpinning the successful implementation of a DPHS system. Little work studied Internet users' demographics when developing DPHS systems, and 39% (291) of studies did not stratify their results by geographic region. A clear methodology by which the results of DPHS can be linked to public health action has yet to be established, as only six (0.8%) studies deployed their system into a PHS context.
PubMed: 33658681
DOI: 10.1038/s41746-021-00407-6 -
Journal of Digital Imaging Jun 2020Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to... (Review)
Review
Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive, and with well-defined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.
Topics: Biological Ontologies; Humans; Language; Liver Diseases
PubMed: 31848894
DOI: 10.1007/s10278-019-00303-2 -
NPJ Digital Medicine Feb 2021Meta-analyses have shown that digital mental health apps can be efficacious in reducing symptoms of depression and anxiety. However, real-world usage of apps is... (Review)
Review
Meta-analyses have shown that digital mental health apps can be efficacious in reducing symptoms of depression and anxiety. However, real-world usage of apps is typically not sustained over time, and no studies systematically examine which features increase sustained engagement with apps or the relationship between engagement features and clinical efficacy. We conducted a systematic search of the literature to identify empirical studies that (1) investigate standalone apps for depression and/or anxiety in symptomatic participants and (2) report at least one measure of engagement. Features intended to increase engagement were categorized using the persuasive system design (PSD) framework and principles of behavioral economics. Twenty-five studies with 4159 participants were included in the analysis. PSD features were commonly used, whereas behavioral economics techniques were not. Smartphone apps were efficacious in treating symptoms of anxiety and depression in randomized controlled trials, with overall small-to-medium effects (g = 0.2888, SE = 0.0999, z(15) = 2.89, p = 0.0119, Q(df = 14) = 41.93, p < 0.0001, I = 66.6%), and apps that employed a greater number of engagement features as compared to the control condition had larger effect sizes (β = 0.0450, SE = 0.0164, t(15) = 2.7344, p = 0.0161). We observed an unexpected negative association between PSD features and engagement, as measured by completion rate (β = -0.0293, SE = 0.0121, t(17) = 02.4142, p = 0.0281). Overall, PSD features show promise for augmenting app efficacy, though engagement, as reflected in study completion, may not be the primary factor driving this association. The results suggest that expanding the use of PSD features in mental health apps may increase clinical benefits and that other techniques, such as those informed by behavioral economics, are employed infrequently.
PubMed: 33574573
DOI: 10.1038/s41746-021-00386-8 -
Folia Phoniatrica Et Logopaedica :... 2021Children with and without speech sound disorders (SSDs) are exposed to different patterns of infant feeding (breast/bottle-feeding) and may or may not engage in... (Review)
Review
BACKGROUND
Children with and without speech sound disorders (SSDs) are exposed to different patterns of infant feeding (breast/bottle-feeding) and may or may not engage in non-nutritive sucking (NNS) (pacifier/digit-sucking). Sucking and speech use similar oral musculature and structures, therefore it is possible that early sucking patterns may impact early speech sound development. The objective of this review is to synthesise the current evidence on the influence of feeding and NNS on the speech sound development of healthy full-term children.
SUMMARY
Electronic databases (PubMed, NHS CRD, EMBASE, MEDLINE) were searched using terms specific to feeding, NNS and speech sound development. All methodologies were considered. Studies were assessed for inclusion and quality by 2 reviewers. Of 1,031 initial results, 751 records were screened, and 5 primary studies were assessed for eligibility, 4 of which were included in the review. Evidence from the available literature on the relationship between feeding, NNS and speech sound development was inconsistent and inconclusive. An association between NNS duration and SSDs was the most consistent finding, reported by 3 of the 4 studies. Quality appraisal was carried out using the Appraisal Tool for Cross-Sectional Studies (AXIS). The included studies were found to be of moderate quality. Key Messages: This review found there is currently limited evidence on the relationship between feeding, NNS and speech sound development. Exploring this unclear relationship is important because of the overlapping physical mechanisms for feeding, NNS and speech production, and therefore the possibility that feeding and/or sucking behaviours may have the potential to impact on speech sound development. Further high-quality research into specific types of SSD using coherent clinically relevant assessment measures is needed to clarify the nature of the association between feeding, NNS and speech sound development, in order to inform and support families and health care professionals.
Topics: Child; Cross-Sectional Studies; Fingersucking; Humans; Infant; Pacifiers; Phonetics; Sucking Behavior
PubMed: 32040950
DOI: 10.1159/000505266 -
Digital Health 2024Wearable technology is used in healthcare to monitor the health of individuals. This study presents an updated systematic literature review of the use of wearable... (Review)
Review
BACKGROUND
Wearable technology is used in healthcare to monitor the health of individuals. This study presents an updated systematic literature review of the use of wearable technology in promoting child and adolescent health, accompanied by recommendations for future research.
METHODS
This review focuses on studies involving children and adolescents aged between 2 and 18 years, regardless of their health condition or disabilities. Studies that were published from 2016 to 2024, and which met the inclusion criteria, were extracted from four academic databases (i.e. PubMed, Cochrane, Embase, and Web of Science) using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. Data on intervention purposes, interventions deployed, intervention duration, measurements, and the main outcomes of the studies were collected.
RESULTS
A total of 53 studies involving 14,852 participants were reviewed. They focused on various aspects, including the ownership and use of wearable devices ( = 3), the feasibility ( = 22), effectiveness ( = 4), and adherence ( = 2) of intervention strategies, or a combination of multiple aspects ( = 22). Among the interventions deployed, Fitbit was the most frequently used, featuring in 26 studies, followed by ActiGraph ( = 11). In intervention studies, the majority of studies focused on pre-morbidity prevention ( = 26) and the treatment of illnesses ( = 20), with limited attention given to postoperative monitoring ( = 4).
CONCLUSIONS
The use of wearable technology by children and adolescents has proven to be both feasible and effective for health promotion. This systematic review summarizes existing research by exploring the use of wearable technology in promoting health across diverse youth populations, including healthy and unhealthy individuals. It examines health promotion at various stages of the disease continuum, including pre-disease prevention, in-disease treatment, and postoperative monitoring. Additionally, the review provides directions for future research.
PubMed: 38868368
DOI: 10.1177/20552076241260507 -
Frontiers in Digital Health 2020The widespread adoption of digital health technologies such as smartphone-based mobile applications, wearable activity trackers and Internet of Things systems has...
The widespread adoption of digital health technologies such as smartphone-based mobile applications, wearable activity trackers and Internet of Things systems has rapidly enabled new opportunities for predictive health monitoring. Leveraging digital health tools to track parameters relevant to human health is particularly important for the older segments of the population as old age is associated with and higher care needs. In order to assess the potential of these digital health technologies to improve health outcomes, it is paramount to investigate which digitally measurable parameters can effectively improve health outcomes among the elderly population. Currently, there is a lack of systematic evidence on this topic due to the inherent heterogeneity of the digital health domain and the lack of clinical validation of both novel prototypes and marketed devices. For this reason, the aim of the current study is to synthesize and systematically analyse which digitally measurable data may be effectively collected through digital health devices to improve health outcomes for older people. Using a modified PICO process and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we provide the results of a systematic review and subsequent meta-analysis of digitally measurable predictors of morbidity, hospitalization, and mortality among older adults aged 65 or older. These findings can inform both technology developers and clinicians involved in the design, development and clinical implementation of digital health technologies for elderly citizens.
PubMed: 34713066
DOI: 10.3389/fdgth.2020.602093 -
Brain and Behavior Jun 2023Multiple sclerosis (MS) is a chronic demyelinating/neurodegenerative disease associated with change in cognitive function (CF) over time. This systematic review aims to... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Multiple sclerosis (MS) is a chronic demyelinating/neurodegenerative disease associated with change in cognitive function (CF) over time. This systematic review aims to describe the instruments used to measure change in CF over time in people with MS (PwMS).
METHODS
PubMed, OVID, Web of Science, and Scopus databases were searched in English until May 2021. Articles were included if they had at least 100 participants and at least a 1-year interval between baseline and last follow-up measurement of CF. Results were quantitatively synthesized, presented in tables and risk of bias was assessed with the Newcastle-Ottawa Scale.
RESULTS
Fifty-seven articles met the inclusion criteria (41,623 PwMS and 1105 controls). An intervention (drug/rehabilitation) was assessed in 22 articles. In the studies that used a test battery, Visual and verbal learning and memory were the most frequently measured domains, but when studies that used test battery or a single test are combined, Information processing speed was the most measured. The Symbol Digit Modalities Test (SDMT) was the most frequently used test as a single test and in a test battery combined. Most studied assessed "change in CF" as cognitive decline defined as 1 or more tests measured as ≥ 1.5 SD from the study control or normative mean in a test battery at baseline and follow-up. Meta-analysis of change in SDMT scores with seven articles indicated a nonstatistically significant -0.03 (95% CI -0.14, 0.09) decrease in mean SDMT score per year.
CONCLUSION
This study highlights the slow rate of measured change in cognition in PwMS and emphasizes the lack of a gold standard test and consistency in measuring cognitive change at the population level. More sensitive testing utilizing multiple domains and longer follow-up may define subgroups where CF change follows different trajectories thus allowing targeted interventions to directly support those where CF is at greatest risk of becoming a clinically meaningful issue.
Topics: Humans; Multiple Sclerosis; Cognition Disorders; Neurodegenerative Diseases; Cognition; Cognitive Dysfunction; Neuropsychological Tests
PubMed: 37062948
DOI: 10.1002/brb3.3009 -
NPJ Digital Medicine Dec 2023Motor Neuron Disease (MND) is a progressive and largely fatal neurodegeneritve disorder with a lifetime risk of approximately 1 in 300. At diagnosis, up to 25% of people... (Review)
Review
Motor Neuron Disease (MND) is a progressive and largely fatal neurodegeneritve disorder with a lifetime risk of approximately 1 in 300. At diagnosis, up to 25% of people with MND (pwMND) exhibit bulbar dysfunction. Currently, pwMND are assessed using clinical examination and diagnostic tools including the ALS Functional Rating Scale Revised (ALS-FRS(R)), a clinician-administered questionnaire with a single item on speech intelligibility. Here we report on the use of digital technologies to assess speech features as a marker of disease diagnosis and progression in pwMND. Google Scholar, PubMed, Medline and EMBASE were systematically searched. 40 studies were evaluated including 3670 participants; 1878 with a diagnosis of MND. 24 studies used microphones, 5 used smartphones, 6 used apps, 2 used tape recorders and 1 used the Multi-Dimensional Voice Programme (MDVP) to record speech samples. Data extraction and analysis methods varied but included traditional statistical analysis, CSpeech, MATLAB and machine learning (ML) algorithms. Speech features assessed also varied and included jitter, shimmer, fundamental frequency, intelligible speaking rate, pause duration and syllable repetition. Findings from this systematic review indicate that digital speech biomarkers can distinguish pwMND from healthy controls and can help identify bulbar involvement in pwMND. Preliminary evidence suggests digitally assessed acoustic features can identify more nuanced changes in those affected by voice dysfunction. No one digital speech biomarker alone is consistently able to diagnose or prognosticate MND. Further longitudinal studies involving larger samples are required to validate the use of these technologies as diagnostic tools or prognostic biomarkers.
PubMed: 38062079
DOI: 10.1038/s41746-023-00959-9 -
The diagnostic and triage accuracy of digital and online symptom checker tools: a systematic review.NPJ Digital Medicine Aug 2022Digital and online symptom checkers are an increasingly adopted class of health technologies that enable patients to input their symptoms and biodata to produce a set of... (Review)
Review
Digital and online symptom checkers are an increasingly adopted class of health technologies that enable patients to input their symptoms and biodata to produce a set of likely diagnoses and associated triage advice. However, concerns regarding the accuracy and safety of these symptom checkers have been raised. This systematic review evaluates the accuracy of symptom checkers in providing diagnoses and appropriate triage advice. MEDLINE and Web of Science were searched for studies that used either real or simulated patients to evaluate online or digital symptom checkers. The primary outcomes were the diagnostic and triage accuracy of the symptom checkers. The QUADAS-2 tool was used to assess study quality. Of the 177 studies retrieved, 10 studies met the inclusion criteria. Researchers evaluated the accuracy of symptom checkers using a variety of medical conditions, including ophthalmological conditions, inflammatory arthritides and HIV. A total of 50% of the studies recruited real patients, while the remainder used simulated cases. The diagnostic accuracy of the primary diagnosis was low across included studies (range: 19-37.9%) and varied between individual symptom checkers, despite consistent symptom data input. Triage accuracy (range: 48.8-90.1%) was typically higher than diagnostic accuracy. Overall, the diagnostic and triage accuracy of symptom checkers are variable and of low accuracy. Given the increasing push towards adopting this class of technologies across numerous health systems, this study demonstrates that reliance upon symptom checkers could pose significant patient safety hazards. Large-scale primary studies, based upon real-world data, are warranted to demonstrate the adequate performance of these technologies in a manner that is non-inferior to current best practices. Moreover, an urgent assessment of how these systems are regulated and implemented is required.
PubMed: 35977992
DOI: 10.1038/s41746-022-00667-w -
Digital Health 2023The development of artificial intelligence (AI), machine learning (ML) and deep learning (DL) has advanced rapidly in the medical field, notably in trauma medicine. We... (Review)
Review
BACKGROUND
The development of artificial intelligence (AI), machine learning (ML) and deep learning (DL) has advanced rapidly in the medical field, notably in trauma medicine. We aimed to systematically appraise the efficacy of AI, ML and DL models for predicting outcomes in trauma triage compared to conventional triage tools.
METHODS
We searched PubMed, MEDLINE, ProQuest, Embase and reference lists for studies published from 1 January 2010 to 9 June 2022. We included studies which analysed the use of AI, ML and DL models for trauma triage in human subjects. Reviews and AI/ML/DL models used for other purposes such as teaching, or diagnosis were excluded. Data was extracted on AI/ML/DL model type, comparison tools, primary outcomes and secondary outcomes. We performed meta-analysis on studies reporting our main outcomes of mortality, hospitalisation and critical care admission.
RESULTS
One hundred and fourteen studies were identified in our search, of which 14 studies were included in the systematic review and 10 were included in the meta-analysis. All studies performed external validation. The best-performing AI/ML/DL models outperformed conventional trauma triage tools for all outcomes in all studies except two. For mortality, the mean area under the receiver operating characteristic (AUROC) score difference between AI/ML/DL models and conventional trauma triage was 0.09, 95% CI (0.02, 0.15), favouring AI/ML/DL models ( = 0.008). The mean AUROC score difference for hospitalisation was 0.11, 95% CI (0.10, 0.13), favouring AI/ML/DL models ( = 0.0001). For critical care admission, the mean AUROC score difference was 0.09, 95% CI (0.08, 0.10) favouring AI/ML/DL models ( = 0.00001).
CONCLUSIONS
This review demonstrates that the predictive ability of AI/ML/DL models is significantly better than conventional trauma triage tools for outcomes of mortality, hospitalisation and critical care admission. However, further research and in particular randomised controlled trials are required to evaluate the clinical and economic impacts of using AI/ML/DL models in trauma medicine.
PubMed: 37822960
DOI: 10.1177/20552076231205736