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PLOS Digital Health Jan 2024Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and...
Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87-96, p< 0.01) and specificity (90%; 95% CI: 85-93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90-94, p< 0.01; and 91%; 95% CI: 88-93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77-85, p< 0.01; and 83%; 95% CI: 76-88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90-96, p < 0.01) and specificity (92%; 95% CI: 89-94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).
PubMed: 38289965
DOI: 10.1371/journal.pdig.0000438 -
The Lancet. Digital Health Mar 2024Digital therapeutics (DTx) are a somewhat novel class of US Food and Drug Administration-regulated software that help patients prevent, manage, or treat disease. Here,... (Review)
Review
Digital therapeutics (DTx) are a somewhat novel class of US Food and Drug Administration-regulated software that help patients prevent, manage, or treat disease. Here, we use natural language processing to characterise registered DTx clinical trials and provide insights into the clinical development landscape for these novel therapeutics. We identified 449 DTx clinical trials, initiated or expected to be initiated between 2010 and 2030, from ClinicalTrials.gov using 27 search terms, and available data were analysed, including trial durations, locations, MeSH categories, enrolment, and sponsor types. Topic modelling of eligibility criteria, done with BERTopic, showed that DTx trials frequently exclude patients on the basis of age, comorbidities, pregnancy, language barriers, and digital determinants of health, including smartphone or data plan access. Our comprehensive overview of the DTx development landscape highlights challenges in designing inclusive DTx clinical trials and presents opportunities for clinicians and researchers to address these challenges. Finally, we provide an interactive dashboard for readers to conduct their own analyses.
Topics: Humans; Natural Language Processing; Smartphone; Software
PubMed: 38395542
DOI: 10.1016/S2589-7500(23)00244-3 -
Digital Health 2024Mental health disorders affect millions of people worldwide. Chatbots are a new technology that can help users with mental health issues by providing innovative... (Review)
Review
INTRODUCTION
Mental health disorders affect millions of people worldwide. Chatbots are a new technology that can help users with mental health issues by providing innovative features. This article aimed to conduct a systematic review of reviews on chatbots in mental health services and synthesized the evidence on the factors influencing patient engagement with chatbots.
METHODS
This study reviewed the literature from 2000 to 2024 using qualitative analysis. The authors conducted a systematic search of several databases, such as PubMed, Scopus, ProQuest, and Cochrane database of systematic reviews, to identify relevant studies on the topic. The quality of the selected studies was assessed using the Critical Appraisal Skills Programme appraisal checklist and the data obtained from the systematic review were subjected to a thematic analysis utilizing the Boyatzis's code development approach.
RESULTS
The database search resulted in 1494 papers, of which 10 were included in the study after the screening process. The quality assessment of the included studies scored the papers within a moderate level. The thematic analysis revealed four main themes: chatbot design, chatbot outcomes, user perceptions, and user characteristics.
CONCLUSION
The research proposed some ways to use color and music in chatbot design. It also provided a systematic and multidimensional analysis of the factors, offered some insights for chatbot developers and researchers, and highlighted the potential of chatbots to improve patient-centered and person-centered care in mental health services.
PubMed: 38655378
DOI: 10.1177/20552076241247983 -
NPJ Digital Medicine Mar 2024The effects of technology-supported behavior change interventions for reducing sodium intake on health outcomes in adults are inconclusive. Effective intervention... (Review)
Review
The effects of technology-supported behavior change interventions for reducing sodium intake on health outcomes in adults are inconclusive. Effective intervention characteristics associated with sodium reduction have yet to be identified. A systematic review and meta-analysis were conducted, searching randomized controlled trials (RCTs) published between January 2000 and April 2023 across 5 databases (PROSPERO: CRD42022357905). Meta-analyses using random-effects models were performed on 24-h urinary sodium (24HUNa), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Subgroup analysis and meta-regression of 24HUNa were performed to identify effective intervention characteristics. Eighteen RCTs involving 3505 participants (51.5% female, mean age 51.6 years) were included. Technology-supported behavior change interventions for reducing sodium intake significantly reduced 24HUNa (mean difference [MD] -0.39 gm/24 h, 95% confidence interval [CI] -0.50 to -0.27; I = 24%), SBP (MD -2.67 mmHg, 95% CI -4.06 to -1.29; I = 40%), and DBP (MD -1.39 mmHg, 95% CI -2.31 to -0.48; I = 31%), compared to control conditions. Interventions delivered more frequently (≤weekly) were associated with a significantly larger effect size in 24HUNa reduction compared to less frequent interventions (>weekly). Other intervention characteristics, such as intervention delivery via instant messaging and participant-family dyad involvement, were associated with larger, albeit non-significant, effect sizes in 24HUNa reduction when compared to other subgroups. Technology-supported behavior change interventions aimed at reducing sodium intake were effective in reducing 24HUNa, SBP, and DBP at post-intervention. Effective intervention characteristics identified in this review should be considered to develop sodium intake reduction interventions and tested in future trials, particularly for its long-term effects.
PubMed: 38499729
DOI: 10.1038/s41746-024-01067-y -
Frontiers in Digital Health 2023An increasing number of mHealth interventions aim to contribute to mental healthcare of which interventions that foster cognitive reappraisal may be particularly... (Review)
Review
BACKGROUND
An increasing number of mHealth interventions aim to contribute to mental healthcare of which interventions that foster cognitive reappraisal may be particularly effective.
OBJECTIVES
To evaluate the efficacy of mHealth interventions enhancing cognitive reappraisal to improve mental health in adult populations.
METHODS
The literature search (four databases) yielded 30 eligible randomized controlled trials (comprising 3,904 participants). We performed a multi-level meta-analysis to examine differences between intervention and comparator conditions at post-intervention assessment. Moderator analyses were conducted for potential moderator variables (e.g., type of comparators).
RESULTS
Most interventions were CBT-based with other training components in addition to cognitive reappraisal. We found preliminary evidence for a small to medium effect favouring mHealth interventions to enhance cognitive reappraisal over comparators, (SMD) = 0.34, = .002. When analysing single symptoms, there was evidence for a small to medium effect of mHealth interventions on anxiety and depressive symptoms, but not for psychological distress and well-being. All analyses showed substantial heterogeneity. Moderator analyses revealed evidence for more favourable effects in studies with passive comparators. There was an overall high risk of bias in most of the studies.
CONCLUSIONS
We found preliminary evidence for a small to medium effect of mHealth interventions including a cognitive reappraisal component to improve mental health. However, most of the interventions were complex (i.e., reappraisal was provided alongside other components), which prevents us from examining reappraisal-specific effects beyond general mental health promotion in mHealth. Dismantling studies examining the effects of single intervention components are warranted to corroborate these promising results.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=142149, identifier [CRD42019142149].
PubMed: 37927578
DOI: 10.3389/fdgth.2023.1253390 -
Frontiers in Digital Health 2024Amyotrophic Lateral Sclerosis (ALS) significantly impacts the lives of people with the diagnosis and their families. A supportive social environment is important for... (Review)
Review
BACKGROUND
Amyotrophic Lateral Sclerosis (ALS) significantly impacts the lives of people with the diagnosis and their families. A supportive social environment is important for people with ALS to adopt effective coping strategies and health behaviours, and reduce depressive symptoms. Peer support can provide a supportive social environment and can happen in-person and online. Advantages of online peer support are that people can engage from their own home, at their own time and pace, and that it offers a variety of different platforms and modes of communication.
OBJECTIVES
To (1) explore the benefits and challenges of online peer support for people with ALS, and (2) identify successful elements of online peer support for people with ALS.
METHODS
The method selected for this systematic review was a narrative synthesis. Six databases were systematically searched in April 2020 for articles published between 1989 and 2020. The search was updated in June 2022. The quality of the included studies was assessed with the Critical Appraisal Skills Programme qualitative research checklist.
RESULTS
10,987 unique articles were identified through the systematic database search. Of those, 9 were included in this review. One of the main benefits of online peer support was that people could communicate using text rather than needing verbal communication, which can be challenging for some with ALS. Successful elements included using profile pages and graphics to identify others with similar or relevant experiences. Challenges included ALS symptoms which could make it difficult to use technological devices.
CONCLUSIONS
Peer support can provide a non-judgmental and supportive environment for people with ALS, in which they can exchange experiences and emotional support, which can help people in developing adaptive coping strategies. However, ALS symptoms may make it more difficult for people to use technological devices and engage in online peer support. More research is needed to identify what kind of specific barriers people with ALS experience, and how these could be overcome.
PubMed: 38357638
DOI: 10.3389/fdgth.2024.1138530 -
NPJ Digital Medicine May 2024Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the... (Review)
Review
Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.
PubMed: 38724610
DOI: 10.1038/s41746-024-01117-5 -
Digital Health 2024Digital health is described as the use and development of all types of digital technologies to improve health outcomes. It could be used to prevent medication errors, a... (Review)
Review
OBJECTIVE
Digital health is described as the use and development of all types of digital technologies to improve health outcomes. It could be used to prevent medication errors, a priority for health systems worldwide. However, the adoption of such tools remains slow. This study aims to identify factors (attitudes, knowledge and beliefs) acting as barriers and/or facilitators reported by healthcare professionals (HCPs) for the adoption of digital health-related tools for medication appropriateness.
METHODS
A systematic review was performed by searching the literature in the MEDLINE PubMed, and EMBASE scientific databases for original articles regarding qualitative and quantitative data.
RESULTS
Fifteen articles were included and a total of 125 barriers and 108 facilitators were identified, consolidated and categorized into technical (n = 48), organizational (n = 12), economical (n = 4), user-related (n = 34), and patient-related (n = 8) components. The most often reported barriers and facilitators were technical component-related ones concerning the need for additional training (n = 6), the time consumed (n = 6), and the easy way of using or learning how to use the tools (n = 9), respectively. Regarding setting analysis, agreement with clinical decision recommendations and impact on the doctor-patient relationship were more valued in primary care, while the user interface and system design were in the hospital.
CONCLUSIONS
The barriers and facilitators identified in this study provide relevant information to developers and it can be used as a starting point for the designing of successful digital health-related tools, specifically related to medication appropriateness. Future research includes economic evaluation-focused studies and in-depth case studies of specific barriers and facilitators.
PubMed: 38250145
DOI: 10.1177/20552076231225133 -
Effectiveness of telehealth versus in-person care during the COVID-19 pandemic: a systematic review.NPJ Digital Medicine Jun 2024In this systematic review, we compared the effectiveness of telehealth with in-person care during the pandemic using PubMed, CINAHL, PsycINFO, and the Cochrane Central... (Review)
Review
In this systematic review, we compared the effectiveness of telehealth with in-person care during the pandemic using PubMed, CINAHL, PsycINFO, and the Cochrane Central Register of Controlled Trials from March 2020 to April 2023. We included English-language, U.S.-healthcare relevant studies comparing telehealth with in-person care conducted after the onset of the pandemic. Two reviewers independently screened search results, serially extracted data, and independently assessed the risk of bias and strength of evidence. We identified 77 studies, the majority of which (47, 61%) were judged to have a serious or high risk of bias. Differences, if any, in healthcare utilization and clinical outcomes between in-person and telehealth care were generally small and/or not clinically meaningful and varied across the type of outcome and clinical area. For process outcomes, there was a mostly lower rate of missed visits and changes in therapy/medication and higher rates of therapy/medication adherence among patients receiving an initial telehealth visit compared with those receiving in-person care. However, the rates of up-to-date labs/paraclinical assessment were also lower among patients receiving an initial telehealth visit compared with those receiving in-person care. Most studies lacked a standardized approach to assessing outcomes. While we refrain from making an overall conclusion about the performance of telehealth versus in-person visits the use of telehealth is comparable to in-person care across a variety of outcomes and clinical areas. As we transition through the COVID-19 era, models for integrating telehealth with traditional care become increasingly important, and ongoing evaluations of telehealth will be particularly valuable.
PubMed: 38879682
DOI: 10.1038/s41746-024-01152-2 -
Digital Health 2024Despite the well-established health benefits of physical activity, a large population of older adults still maintain sedentary life style or physical inactivity. This... (Review)
Review
BACKGROUND
Despite the well-established health benefits of physical activity, a large population of older adults still maintain sedentary life style or physical inactivity. This network meta-analysis (NMA) aimed to compare the effectiveness of wearable activity tracker-based intervention (WAT), electronic and mobile health intervention (E&MH), structured exercise program intervention (SEP), financial incentive intervention (FI) on promoting physical activity and reducing sedentary time in older adults.
METHODS
The systematic review based on PRISMA guidelines, a systematic literature search of PubMed, Web of Science, Google Scholar, EMbase, Cochrane Library, Scopus were searched from inception to December 10 2022. The randomized controlled trials (RCT) were included. Two reviewers independently conducted study selection, data extraction, risk of bias and certainty of evidence assessment. The effect measures were standard mean differences (SMD) and 95% confidence interval (CI) in daily steps, moderate-to-vigorous physical activity (MVPA) and sedentary time.
RESULTS
A total of 69 studies with 14,120 participants were included in the NMA. Among these included studies, the results of daily steps, MVPA and sedentary time was reported by 55, 25 and 15 studies, respectively. The NMA consistency model analysis suggested that the following interventions had the highest probability (surface under the cumulative ranking, SUCRA) of being the best when compared with control: FI + WAT for daily steps (SUCRA = 96.6%; SMD = 1.32, 95% CI:0.77, 1.86), WAT + E&MH + SEP for MVPA (SUCRA = 91.2%; SMD = 0.94, 95% CI: 0.36, 1.52) and WAT + E&MH + SEP for sedentary time (SUCRA = 80.3%; SMD = -0.50, 95% CI: -0.87, -0.14). The quality of the evidences of daily steps, MVPA and sedentary time was evaluated by very low, very low and low, respectively.
CONCLUSIONS
In this NMA, there's low quality evidence that financial incentive combined with wearable activity tracker is the most effective intervention for increasing daily steps of older adults, wearable activity tracker combined with electronic and mobile health and structured exercise program is the most effective intervention to help older adults to increase MVPA and reduce sedentary time.
PubMed: 38601186
DOI: 10.1177/20552076241239182