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Frontiers in Digital Health 2023This review focuses on studies about digital health interventions in sub-Saharan Africa. Digital health interventions in sub-Saharan Africa are increasingly adopting... (Review)
Review
BACKGROUND
This review focuses on studies about digital health interventions in sub-Saharan Africa. Digital health interventions in sub-Saharan Africa are increasingly adopting gender-transformative approaches to address factors that derail women's access to maternal healthcare services. However, there remains a paucity of synthesized evidence on gender-transformative digital health programs for maternal healthcare and the corresponding research, program and policy implications. Therefore, this systematic review aims to synthesize evidence of approaches to transformative gender integration in digital health programs (specifically mHealth) for maternal health in sub-Saharan Africa.
METHOD
The following key terms "mobile health", "gender", "maternal health", "sub-Saharan Africa" were used to conduct electronic searches in the following databases: PsycInfo, EMBASE, Medline (OVID), CINAHL, and Global Health databases. The method and results are reported as consistent with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Data synthesis followed a convergent approach for mixed-method systematic review recommended by the JBI (Joanna Briggs Institute).
RESULTS
Of the 394 studies retrieved from the databases, 11 were included in the review. Out of these, six studies were qualitative in nature, three were randomized control trials, and two were mixed-method studies. Findings show that gender transformative programs addressed one or more of the following categories: (1) gender norms/roles/relations, (2) women's specific needs, (3) causes of gender-based health inequities, (4) ways to transform harmful gender norms, (5) promoting gender equality, (6) progressive changes in power relationships between women and men. The most common mHealth delivery system was text messages via short message service on mobile phones. The majority of mHealth programs for maternal healthcare were focused on reducing unintended pregnancies through the promotion of contraceptive use. The most employed gender transformative approach was a focus on women's specific needs.
CONCLUSION
Findings from gender transformative mHealth programs indicate positive results overall. Those reporting negative results indicated the need for a more explicit focus on gender in mHealth programs. Highlighting gender transformative approaches adds to discussions on how best to promote mHealth for maternal health through a gender transformative lens and provides evidence relevant to policy and research.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO CRD42023346631.
PubMed: 38026837
DOI: 10.3389/fdgth.2023.1263488 -
Digital Health 2024Smartphone apps (apps) are widely recognised as promising tools for improving access to mental healthcare. However, a key challenge is the development of digital... (Review)
Review
OBJECTIVE
Smartphone apps (apps) are widely recognised as promising tools for improving access to mental healthcare. However, a key challenge is the development of digital interventions that are acceptable to end users. Co-production with providers and stakeholders is increasingly positioned as the gold standard for improving uptake, engagement, and healthcare outcomes. Nevertheless, clear guidance around the process of co-production is lacking. The objectives of this review were to: (i) present an overview of the methods and approaches to co-production when designing, producing, and evaluating digital mental health interventions; and (ii) explore the barriers and facilitators affecting co-production in this context.
METHODS
A pre-registered (CRD42023414007) systematic review was completed in accordance with The Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Five databases were searched. A co-produced bespoke quality appraisal tool was developed with an expert by experience to assess the quality of the co-production methods and approaches. A narrative synthesis was conducted.
RESULTS
Twenty-six studies across 24 digital mental health interventions met inclusion criteria. App interventions were rarely co-produced with end users throughout all stages of design, development, and evaluation. Co-producing digital mental health interventions added value by creating culturally sensitive and acceptable interventions. Reported challenges included resource issues exacerbated by the digital nature of the intervention, variability across stakeholder suggestions, and power imbalances between stakeholders and researchers.
CONCLUSIONS
Variation in approaches to co-producing digital mental health interventions is evident, with inconsistencies between stakeholder groups involved, stage of involvement, stakeholders' roles and methods employed.
PubMed: 38665886
DOI: 10.1177/20552076241239172 -
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 -
NPJ Digital Medicine Apr 2024The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical... (Review)
Review
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
PubMed: 38594408
DOI: 10.1038/s41746-024-01031-w -
Frontiers in Digital Health 2023Computer-mediated care is becoming increasingly popular, but little research has been done on it and its effects on emotion-related outcomes. This systematic literature... (Review)
Review
INTRODUCTION
Computer-mediated care is becoming increasingly popular, but little research has been done on it and its effects on emotion-related outcomes. This systematic literature review aims to create an overview that addresses the research question: "Is there a relationship between computer-mediated care and emotional expression, perception, and emotional and (long-term) emotion outcomes?"
METHOD
This systematic literature review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and used five eligibility criteria, namely, (1) participants: adults seeking support; (2) intervention: eHealth; (3) diagnostic criteria: transdiagnostic concept of difficulty identifying, expressing, and/or regulating emotions (e.g., alexithymia); (4) comparator: either face-to-face care or no comparator; and (5) study design: quantitative studies or qualitative studies. Quality was assessed using the QualSyst tool.
RESULTS
The analysis includes 25 research papers. Self-paced interventions appear to have a positive effect on emotion regulation. Videoconferencing interventions improved emotion regulation from before to after treatment but worsened emotion regulation compared with face-to-face treatment.
DISCUSSION
The lack of variation in the modalities studied and the emotion measurements used make it difficult to draw responsible conclusions. Future research should examine how different modalities affect the real-time communication of emotions and how non-verbal cues influence this.
PubMed: 37720162
DOI: 10.3389/fdgth.2023.1216268 -
Digital Health 2023Augmented reality (AR) is a relatively new technology that merges virtual and physical environments, augmenting one's perception of reality. AR creates a... (Review)
Review
OBJECTIVE
Augmented reality (AR) is a relatively new technology that merges virtual and physical environments, augmenting one's perception of reality. AR creates a computer-generated environment that evokes a unique perception of reality, where real and virtual objects are registered with one another, which operates interactively and in real time. Recently, the medical application of AR technology has dramatically increased with other assisted technologies, from training to clinical practice. The ability to manipulate the real environment extensively has given AR interventions an advantage over traditional approaches. In this study, we aim to conduct a systematic review of the use of AR to have a better understanding of how the use of AR may affect patients with mental health-related conditions when combined with gamification.
METHOD
This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines by searching Pubmed and Web of Science databases.
RESULTS AND CONCLUSION
We identified 48 relevant studies that fulfill the criteria. The studies were grouped into four categories: Neurodevelopmental disorders, anxiety and phobia, psychoeducation & well-being, and procedural & pain management. Our results revealed the effectiveness of AR in mental health-related conditions. However, the heterogeneity and small sample sizes demonstrate the need for further research with larger sample sizes and high-quality study designs.
PubMed: 37791140
DOI: 10.1177/20552076231203649 -
NPJ Digital Medicine Nov 2023Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most... (Review)
Review
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
PubMed: 38012349
DOI: 10.1038/s41746-023-00941-5 -
Digital Health 2023Cystic fibrosis causes mucus to build up in the lungs, digestive tract, and other areas. It is the most common chronic lung disease in children and young adults. It... (Review)
Review
BACKGROUND
Cystic fibrosis causes mucus to build up in the lungs, digestive tract, and other areas. It is the most common chronic lung disease in children and young adults. It requires daily medical care. Before the COVID-19 pandemic, telerehabilitation and telehealth were used, but it was after this that there was a boom in these types of assistance in order to continue caring for cystic fibrosis patients.
OBJECTIVE
The objective is to evaluate the effect of telemedicine programs in people with cystic fibrosis.
METHODS
For the search, the PubMed, Scopus, Web of Science, PEDro, Cochrane, and CINAHL databases were used. Randomized controlled trials, pilot studies, and clinical trials have been included. The exclusion criteria have considered that the population did not have another active disease or that telemedicine was not used as the main intervention. This study follows the PRISMA statement and has been registered in the PROSPERO database (CRD42021257647).
RESULTS
A total of 11 articles have been included in the systematic review. No improvements have been found in quality of life, forced expiratory volume, and forced vital capacity. Good results have been found in increasing physical activity and early detection of exacerbations. Adherence and satisfaction are very positive and promising.
CONCLUSIONS
Despite not obtaining significant improvements in some of the variables, it should be noted that the adherence and satisfaction of both patients and workers reinforce the use of this type of care. Future studies are recommended in which to continue investigating this topic.
PubMed: 37654722
DOI: 10.1177/20552076231197023 -
Clinical Otolaryngology : Official... Jan 2024Technological advancements in mobile audiometry (MA) have enabled hearing assessment using tablets and smartphones. This systematic review (PROSPERO ID: CRD42021274761)... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVES
Technological advancements in mobile audiometry (MA) have enabled hearing assessment using tablets and smartphones. This systematic review (PROSPERO ID: CRD42021274761) aimed to identify MA options available to health providers, assess their accuracy in measuring hearing thresholds, and explore factors that might influence their accuracy.
DESIGN AND SETTING
A systematic search of online databases including PubMed, Embase, Cochrane, Evidence Search and Dynamed was conducted on 13th December 2021, and repeated on 30th October 2022, using appropriate Medical Subject Headings (MeSH) terms. Eligible studies reported the use of MA to determine hearing thresholds and compared results to conventional pure-tone audiometry (CA). Studies investigating MA for hearing screening (i.e. reporting just pass/fail) were ineligible for inclusion. Two authors independently reviewed studies, extracted data, and assessed methodological quality and risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.
PARTICIPANTS
Adults and children, with and without diagnosis of hearing impairment.
MAIN OUTCOME MEASURES
A meta-analysis was performed to obtain the mean difference between thresholds measured using MA and CA in dB HL.
RESULTS
Searches returned 858 articles. After systematic review, 17 articles including 1032 participants were analysed. The most used software application was Shoebox (6/17) followed by Hearing Test (3/17), then HearTest (2/17). Tablet computers were used in ten studies, smartphones in six, and a computer in one. The mean difference between MA and CA thresholds was 1.36 dB (95% CI, 0.07-2.66, p = 0.04). Significant differences between mobile audiometry (MA) and conventional audiometry (CA) thresholds were observed in thresholds measured at 500Hz, in children, when MA was conducted in a sound booth, and when MA was self-administered. However, these differences did not exceed the clinically significant threshold of 10 decibels (dB). Included studies exhibited high levels of heterogeneity, high risk of bias and low concerns about applicability.
CONCLUSIONS
MA compares favourably to CA in measuring hearing thresholds and has role in providing access to hearing assessment in situations where CA is not available or feasible. Future studies should prioritize the integration of pure-tone threshold assessment with additional tests, such as Speech Recognition and Digits-in-Noise, for a more rounded evaluation of hearing ability, assesses acceptability and feasibility, and the cost-effectiveness of MA in non-specialist settings.
Topics: Adult; Child; Humans; Auditory Threshold; Hearing; Hearing Loss; Audiometry; Audiometry, Pure-Tone; Smartphone
PubMed: 37828806
DOI: 10.1111/coa.14107 -
Digital Health 2023During the Coronavirus Disease 2019 (COVID-19) pandemic, digital health technologies (DHTs) became increasingly important, especially for older adults. The objective of... (Review)
Review
OBJECTIVE
During the Coronavirus Disease 2019 (COVID-19) pandemic, digital health technologies (DHTs) became increasingly important, especially for older adults. The objective of this systematic review was to synthesize evidence on the rapid implementation and use of DHTs among older adults during the COVID-19 pandemic.
METHODS
A structured, electronic search was conducted on 9 November 2021, and updated on 5 January 2023, among five databases to select DHT interventional studies conducted among older adults during the pandemic. The bias of studies was assessed using Version 2 of the Cochrane Risk-of-Bias Tool for randomized trials (RoB 2) and Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I).
RESULTS
Among 20 articles included in the review, 14 (70%) focused on older adults with chronic diseases or symptoms, such as dementia or cognitive impairment, type 2 diabetes, and obesity. DHTs included traditional telehealth interventions via telephone, video, and social media, as well as emerging technologies such as Humanoid Robot and Laser acupuncture teletherapy. Using RoB 2 and ROBINS-I, four studies (20%) were evaluated as high or serious overall risk of bias. DHTs have shown to be effective, feasible, acceptable, and satisfactory for older adults during the COVID-19 pandemic compared to usual care. In addition, some studies also highlighted challenges with technology, hearing difficulties, and communication barriers within the vulnerable population.
CONCLUSIONS
During the COVID-19 pandemic, DHTs had the potential to improve various health outcomes and showed benefits for older adults' access to health care services.
PubMed: 37529545
DOI: 10.1177/20552076231191050