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NPJ Digital Medicine Dec 2023Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage,... (Review)
Review
Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs' effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge's g 0.64 [95% CI 0.17-1.12]) and distress (Hedge's g 0.7 [95% CI 0.18-1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge's g 0.32 [95% CI -0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.
PubMed: 38114588
DOI: 10.1038/s41746-023-00979-5 -
The Lancet. Digital Health Dec 2023Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease... (Meta-Analysis)
Meta-Analysis
Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.
BACKGROUND
Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps.
METHODS
We conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18-90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I>50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052).
FINDINGS
We identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69-0·85]) and there was significant heterogeneity (I 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74-0·80]) with significant degrees of heterogeneity (I 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias.
INTERPRETATION
There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility.
FUNDING
None.
Topics: Adult; Humans; Reproducibility of Results; Deep Learning; Quality of Life; Pulmonary Disease, Chronic Obstructive; Prognosis
PubMed: 38000872
DOI: 10.1016/S2589-7500(23)00177-2 -
Journal of Hand and Microsurgery Aug 2020There is a lack of consensus on what the critical outcomes in replantation are and how best to measure them. This review aims to identify all reported outcomes and... (Review)
Review
There is a lack of consensus on what the critical outcomes in replantation are and how best to measure them. This review aims to identify all reported outcomes and respective outcome measures used in digital replantation. Randomized controlled trials, cohort studies, and single-arm observational studies of adults undergoing replantation with at least one well-described outcome or outcome measure were identified. Primary outcomes were classified into six domains, and outcome measures were classified into eight domains. The clinimetric properties were identified and reported. A total of 56 observational studies met the inclusion criteria. In total, 29 continuous and 29 categorical outcomes were identified, and 87 scales and instruments were identified. The most frequently used outcomes were survival of replanted digit, sensation, and time in hospital. Outcomes and measures were most variable in domains of viability, quality of life, and motor function. Only eight measures used across these domains were validated and proven reliable. Lack of consensus creates an obstacle to reporting, understanding, and comparing the effectiveness of various replantation strategies.
PubMed: 33335363
DOI: 10.1055/s-0040-1701324 -
Frontiers in Digital Health 2022Pain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment.... (Review)
Review
IMPORTANCE
Pain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment. Cognitive/behavioural management approaches and interventional pain management strategies are approaches that have been used to assist with the management of chronic pain. Accurate data collection and reporting treatment outcomes are vital to addressing the challenges faced. In light of this, we conducted a systematic evaluation of the current digital application landscape within chronic pain medicine.
OBJECTIVE
The primary objective was to consider the prevalence of digital application usage for chronic pain management. These digital applications included mobile apps, web apps, and chatbots.
DATA SOURCES
We conducted searches on PubMed and ScienceDirect for studies that were published between 1st January 1990 and 1st January 2021.
STUDY SELECTION
Our review included studies that involved the use of digital applications for chronic pain conditions. There were no restrictions on the country in which the study was conducted. Only studies that were peer-reviewed and published in English were included. Four reviewers had assessed the eligibility of each study against the inclusion/exclusion criteria. Out of the 84 studies that were initially identified, 38 were included in the systematic review.
DATA EXTRACTION AND SYNTHESIS
The AMSTAR guidelines were used to assess data quality. This assessment was carried out by 3 reviewers. The data were pooled using a random-effects model.
MAIN OUTCOMES AND MEASURES
Before data collection began, the primary outcome was to report on the standard mean difference of digital application usage for chronic pain conditions. We also recorded the type of digital application studied (e.g., mobile application, web application) and, where the data was available, the standard mean difference of pain intensity, pain inferences, depression, anxiety, and fatigue.
RESULTS
38 studies were included in the systematic review and 22 studies were included in the meta-analysis. The digital interventions were categorised to web and mobile applications and chatbots, with pooled standard mean difference of 0.22 (95% CI: -0.16, 0.60), 0.30 (95% CI: 0.00, 0.60) and -0.02 (95% CI: -0.47, 0.42) respectively. Pooled standard mean differences for symptomatologies of pain intensity, depression, and anxiety symptoms were 0.25 (95% CI: 0.03, 0.46), 0.30 (95% CI: 0.17, 0.43) and 0.37 (95% CI: 0.05, 0.69), respectively. A sub-group analysis was conducted on pain intensity due to the heterogeneity of the results ( = 82.86%; = 0.02). After stratifying by country, we found that digital applications were more likely to be effective in some countries (e.g., United States, China) than others (e.g., Ireland, Norway).
CONCLUSIONS AND RELEVANCE
The use of digital applications in improving pain-related symptoms shows promise, but further clinical studies would be needed to develop more robust applications.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/, identifier: CRD42021228343.
PubMed: 36405414
DOI: 10.3389/fdgth.2022.850601 -
Sleep Medicine: X Dec 2023Insomnia is a common disease, and the application of various types of sleeping pills for cognitive impairment is controversial, especially as different doses can lead to... (Review)
Review
BACKGROUND
Insomnia is a common disease, and the application of various types of sleeping pills for cognitive impairment is controversial, especially as different doses can lead to different effects. Therefore, it is necessary to evaluate the cognitive impairment caused by different sleeping pills to provide a theoretical basis for guiding clinicians in the selection of medication regimens.
OBJECTIVE
To evaluate whether various different doses (low, medium and high) of anti-insomnia drugs, such as the dual-orexin receptor antagonist (DORA), zopiclone, eszopiclone and zolpidem, induce cognitive impairment.
METHODS
The PubMed, Embase, Scopus, Cochrane Library, and Google Scholar databases were searched from inception to September 20th, 2022 for keywords in randomized controlled trials (RCTs) to evaluate the therapeutic effects of DORA, eszopiclone, zopiclone and zolpidem on sleep and cognitive function. The primary outcomes were indicators related to cognitive characteristics, including scores on the Digit Symbol Substitution Test (DSST) and daytime alertness. The secondary outcomes were the indicators associated with sleep and adverse events. Continuous variables were expressed as the standard mean difference (SMD). Data were obtained through GetData 2.26 and analyzed by Stata v.15.0.
RESULTS
A total of 8702 subjects were included in 29 studies. Eszopiclone significantly increased the daytime alertness score (SMD = 3.00, 95 % CI: 1.86 to 4.13) compared with the placebo, and eszopiclone significantly increased the daytime alertness score (SMD = 4.21, 95 % CI: 1.65 to 6.77; SMD = 3.95, 95 % CI: 1.38 to 6.51; SMD = 3.26, 95 % CI: 0.38 to 6.15; and SMD = 3.23, 95 % CI: 0.34 to 6.11) compared with zolpidem, zolpidem, DORA, and eszopiclone, respectively. Compared with the placebo, zopiclone, zolpidem, and eszopiclone, DORA significantly increased the TST (SMD = 2.39, 95 % CI: 1.11 to 3.67; SMD = 6.00, 95 % CI: 2.73 to 9.27; SMD = 1.89, 95 % CI: 0.90 to 2.88; and SMD = 1.70, 95 % CI: 0.42 to 2.99, respectively).
CONCLUSION
We recommend DORA as the best intervention for insomnia because it was highly effective in inducing and maintaining sleep without impairing cognition. Although zolpidem had a more pronounced effect on sleep maintenance, this drug is better for short-term use. Eszopiclone and zopiclone improved sleep, but their cognitive effects have yet to be verified.
PubMed: 38149178
DOI: 10.1016/j.sleepx.2023.100094 -
Digital Health 2023Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for... (Review)
Review
OBJECTIVE
Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases.
METHODS
A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection.
RESULTS
The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance.
CONCLUSIONS
This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.
PubMed: 37214662
DOI: 10.1177/20552076231173569 -
NPJ Digital Medicine Aug 2023Global Human papillomavirus (HPV) vaccination rates remain low despite available WHO-approved vaccines. Digital interventions for promoting vaccination uptake offer a...
Global Human papillomavirus (HPV) vaccination rates remain low despite available WHO-approved vaccines. Digital interventions for promoting vaccination uptake offer a scalable and accessible solution to this issue. Here we report a systematic review and meta-analysis examining the efficacy of digital interventions, comparing educational and reminder approaches, for promoting HPV vaccination uptake (HVU). This study also identifies factors influencing the effectiveness of these interventions. We searched PubMed, PsycInfo, Web of Science, and the Cochrane Library from each database's inception to January 2023. Three raters independently evaluate the studies using a systematic and blinded method for resolving disagreements. From 1929 references, 34 unique studies (281,280 unique participants) have sufficient data. Client reminder (OR, 1.41; 95% CI, 1.23-1.63; P < 0.001), provider reminder (OR, 1.39; 95% CI, 1.11-1.75; P = 0.005), provider education (OR, 1.18; 95% CI, 1.05-1.34; P = 0.007), and client education plus reminder interventions (OR, 1.29; 95% CI, 1.04-1.59; P = 0.007) increase HVU, whereas client education interventions do not (OR, 1.08; 95% CI, 0.92-1.28; P = 0.35). Digital intervention effectiveness varies based on participants' gender and the digital platform used. Interventions targeting male or mixed-gender participants demonstrate greater benefit, and reminder platforms (SMS, preference reminders, or electronic health record alerts) are more effective in increasing HVU. Digital interventions, particularly client and provider reminders, along with provider education, prove significantly more effective than client education alone. Incorporating digital interventions into healthcare systems can effectively promote HPV vaccination uptake. Reminder interventions should be prioritized for promoting HVU.
PubMed: 37644090
DOI: 10.1038/s41746-023-00912-w -
Psychological Medicine Aug 2022Persons at clinical high-risk for psychosis (CHR) are characterised by specific neurocognitive deficits. However, the course of neurocognitive performance during the... (Meta-Analysis)
Meta-Analysis Review
Persons at clinical high-risk for psychosis (CHR) are characterised by specific neurocognitive deficits. However, the course of neurocognitive performance during the prodromal period and over the onset of psychosis remains unclear. The aim of this meta-analysis was to synthesise results from follow-up studies of CHR individuals to examine longitudinal changes in neurocognitive performance. Three electronic databases were systematically searched to identify articles published up to 31 December 2021. Thirteen studies met inclusion criteria. Study effect sizes (Hedges' ) were calculated and pooled for each neurocognitive task using random-effects meta-analyses. We examined whether changes in performance between baseline and follow-up assessments differed between: (1) CHR and healthy control (HC) individuals, and (2) CHR who did (CHR-T) and did not transition to psychosis (CHR-NT). Meta-analyses found that HC individuals had greater improvements in performance over time compared to CHR for letter fluency ( = -0.32, = 0.029) and digit span ( = -0.30, = 0.011) tasks. Second, there were differences in longitudinal performance of CHR-T and CHR-NT in trail making test A (TMT-A) ( = 0.24, = 0.014) and symbol coding ( = -0.51, = 0.011). Whilst CHR-NT improved in performance on both tasks, CHR-T improved to a lesser extent in TMT-A and had worsened performance in symbol coding over time. Together, neurocognitive performance generally improved in all groups at follow-up. Yet, evidence suggested that improvements were less pronounced for an overall CHR group, and specifically for CHR-T, in processing speed tasks which may be a relevant domain for interventions aimed to enhance neurocognition in CHR populations.
Topics: Humans; Neuropsychological Tests; Disease Progression; Psychotic Disorders; Prodromal Symptoms; Cognition Disorders; Longitudinal Studies
PubMed: 35821623
DOI: 10.1017/S0033291722001830 -
NPJ Digital Medicine Jan 2022Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in... (Review)
Review
Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991-1996) to 118 (2016-2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61-1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.
PubMed: 35046493
DOI: 10.1038/s41746-021-00552-y -
Frontiers in Digital Health 2023This systematic review aims to assess the effectiveness of virtual reality (VR) and gamification interventions in addressing anxiety and depression. The review also... (Review)
Review
This systematic review aims to assess the effectiveness of virtual reality (VR) and gamification interventions in addressing anxiety and depression. The review also seeks to identify gaps in the current VR treatment landscape and provide guidelines for future research and development. A systematic literature search was conducted using Scopus, Web of Science, and PubMed databases, focusing on studies that utilized VR and gamification technology to address anxiety and depression disorders. A total of 2,664 studies were initially identified, 15 of those studies fulfilled the inclusion criteria for this systematic review. The efficacy of VR in addressing anxiety and depression was evident across all included studies. However, the diversity among VR interventions highlights the need for further investigation. It is advised to incorporate more diverse participant samples and larger cohorts and explore a broader spectrum of therapeutic approaches within VR interventions for addressing anxiety and depression to enhance the credibility of future research. Additionally, conducting studies in varying socioeconomic contexts would contribute to a more comprehensive understanding of their real-world applicability.
PubMed: 38026832
DOI: 10.3389/fdgth.2023.1239435