Guideline for reporting systematic reviews of outcome measurement instruments (OMIs): PRISMA-COSMIN for OMIs 2024.

PURPOSE: Although comprehensive and widespread guidelines on how to conduct systematic reviews of outcome measurement instruments (OMIs) exist, for example from the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative, key information is often missing in published reports. This article describes the development of an extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline: PRISMA-COSMIN for OMIs 2024. METHODS: The development process followed the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines and included a literature search, expert consultations, a Delphi study, a hybrid workgroup meeting, pilot testing, and an end-of-project meeting, with integrated patient/public involvement. RESULTS: From the literature and expert consultation, 49 potentially relevant reporting items were identified. Round 1 of the Delphi study was completed by 103 panelists, whereas round 2 and 3 were completed by 78 panelists. After 3 rounds, agreement (≥ 67%) on inclusion and wording was reached for 44 items. Eleven items without consensus for inclusion and/or wording were discussed at a workgroup meeting attended by 24 participants. Agreement was reached for the inclusion and wording of 10 items, and the deletion of 1 item. Pilot testing with 65 authors of OMI systematic reviews further improved the guideline through minor changes in wording and structure, finalized during the end-of-project meeting. The final checklist to facilitate the reporting of full systematic review reports contains 54 (sub)items addressing the review's title, abstract, plain language summary, open science, introduction, methods, results, and discussion. Thirteen items pertaining to the title and abstract are also included in a separate abstract checklist, guiding authors in reporting for example conference abstracts. CONCLUSION: PRISMA-COSMIN for OMIs 2024 consists of two checklists (full reports; abstracts), their corresponding explanation and elaboration documents detailing the rationale and examples for each item, and a data flow diagram. PRISMA-COSMIN for OMIs 2024 can improve the reporting of systematic reviews of OMIs, fostering their reproducibility and allowing end-users to appraise the quality of OMIs and select the most appropriate OMI for a specific application. NOTE: In order to encourage its wide dissemination this article is freely accessible on the web sites of the journals: Health and Quality of Life Outcomes; Journal of Clinical Epidemiology; Journal of Patient-Reported Outcomes; Quality of Life Research.

Guideline for reporting systematic reviews of outcome measurement instruments (OMIs): PRISMA-COSMIN for OMIs 2024.

PURPOSE: Although comprehensive and widespread guidelines on how to conduct systematic reviews of outcome measurement instruments (OMIs) exist, for example from the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative, key information is often missing in published reports. This article describes the development of an extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline: PRISMA-COSMIN for OMIs 2024. METHODS: The development process followed the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines and included a literature search, expert consultations, a Delphi study, a hybrid workgroup meeting, pilot testing, and an end-of-project meeting, with integrated patient/public involvement. RESULTS: From the literature and expert consultation, 49 potentially relevant reporting items were identified. Round 1 of the Delphi study was completed by 103 panelists, whereas round 2 and 3 were completed by 78 panelists. After 3 rounds, agreement (≥67%) on inclusion and wording was reached for 44 items. Eleven items without consensus for inclusion and/or wording were discussed at a workgroup meeting attended by 24 participants. Agreement was reached for the inclusion and wording of 10 items, and the deletion of 1 item. Pilot testing with 65 authors of OMI systematic reviews further improved the guideline through minor changes in wording and structure, finalized during the end-of-project meeting. The final checklist to facilitate the reporting of full systematic review reports contains 54 (sub)items addressing the review's title, abstract, plain language summary, open science, introduction, methods, results, and discussion. Thirteen items pertaining to the title and abstract are also included in a separate abstract checklist, guiding authors in reporting for example conference abstracts. CONCLUSION: PRISMA-COSMIN for OMIs 2024 consists of two checklists (full reports; abstracts), their corresponding explanation and elaboration documents detailing the rationale and examples for each item, and a data flow diagram. PRISMA-COSMIN for OMIs 2024 can improve the reporting of systematic reviews of OMIs, fostering their reproducibility and allowing end-users to appraise the quality of OMIs and select the most appropriate OMI for a specific application. NOTE: In order to encourage its wide dissemination this article is freely accessible on the web sites of the journals: Health and Quality of Life Outcomes; Journal of Clinical Epidemiology; Journal of Patient-Reported Outcomes; Quality of Life Research.

Guideline for reporting systematic reviews of outcome measurement instruments (OMIs): PRISMA-COSMIN for OMIs 2024.

PURPOSE: Although comprehensive and widespread guidelines on how to conduct systematic reviews of outcome measurement instruments (OMIs) exist, for example from the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative, key information is often missing in published reports. This article describes the development of an extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline: PRISMA-COSMIN for OMIs 2024. METHODS: The development process followed the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines and included a literature search, expert consultations, a Delphi study, a hybrid workgroup meeting, pilot testing, and an end-of-project meeting, with integrated patient/public involvement. RESULTS: From the literature and expert consultation, 49 potentially relevant reporting items were identified. Round 1 of the Delphi study was completed by 103 panelists, whereas round 2 and 3 were completed by 78 panelists. After 3 rounds, agreement (≥ 67%) on inclusion and wording was reached for 44 items. Eleven items without consensus for inclusion and/or wording were discussed at a workgroup meeting attended by 24 participants. Agreement was reached for the inclusion and wording of 10 items, and the deletion of 1 item. Pilot testing with 65 authors of OMI systematic reviews further improved the guideline through minor changes in wording and structure, finalized during the end-of-project meeting. The final checklist to facilitate the reporting of full systematic review reports contains 54 (sub)items addressing the review's title, abstract, plain language summary, open science, introduction, methods, results, and discussion. Thirteen items pertaining to the title and abstract are also included in a separate abstract checklist, guiding authors in reporting for example conference abstracts. CONCLUSION: PRISMA-COSMIN for OMIs 2024 consists of two checklists (full reports; abstracts), their corresponding explanation and elaboration documents detailing the rationale and examples for each item, and a data flow diagram. PRISMA-COSMIN for OMIs 2024 can improve the reporting of systematic reviews of OMIs, fostering their reproducibility and allowing end-users to appraise the quality of OMIs and select the most appropriate OMI for a specific application. NOTE: In order to encourage its wide dissemination this article is freely accessible on the web sites of the journals: Health and Quality of Life Outcomes; Journal of Clinical Epidemiology; Journal of Patient-Reported Outcomes; Quality of Life Research.

Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes. OBJECTIVE: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA. METHODS: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). RESULTS: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence. CONCLUSIONS: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI's impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

Revealing trends in academic publishing on immersive journalism through a dataset analysis of metaverse and extended technologies from 2017 to 2022.

This article presents the data obtained from a Systematic Literature Review (SLR) on the use of metaverse and extended technologies for immersive journalism [1]. Boolean operators, both in English and Spanish, were used to retrieve scientific literature using Publish or Perish 8 software on Scopus, Web of Science and Google Scholar between 2017 and 2022. After finding all the scientific literature, a methodological process was carried out using selection criteria and following the PRISMA model to obtain a total sample of 61 scientific articles. The DESLOCIS framework was used for the evaluation and quantitative and qualitative analysis of the retrieved data. The first dataset [2] contains the metadata of the retrieved publications according to the phases of the PRISMA statement. The second dataset [3] contains the characteristics of these publications according to the DESLOCIS framework. The data offer the possibility to develop new longitudinal studies and meta-analyzes in the field of immersive journalism.

Guideline for reporting systematic reviews of outcome measurement instruments (OMIs): PRISMA-COSMIN for OMIs 2024.

BACKGROUND AND OBJECTIVE: Although comprehensive and widespread guidelines on how to conduct systematic reviews of outcome measurement instruments (OMIs) exist, for example from the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative, key information is often missing in published reports. This article describes the development of an extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline: PRISMA-COSMIN for OMIs 2024. METHODS: The development process followed the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines and included a literature search, expert consultations, a Delphi study, a hybrid workgroup meeting, pilot testing, and an end-of-project meeting, with integrated patient/public involvement. RESULTS: From the literature and expert consultation, 49 potentially relevant reporting items were identified. Round 1 of the Delphi study was completed by 103 panelists, whereas round 2 and 3 were completed by 78 panelists. After 3 rounds, agreement (≥67%) on inclusion and wording was reached for 44 items. Eleven items without consensus for inclusion and/or wording were discussed at a workgroup meeting attended by 24 participants. Agreement was reached for the inclusion and wording of 10 items, and the deletion of 1 item. Pilot testing with 65 authors of OMI systematic reviews further improved the guideline through minor changes in wording and structure, finalized during the end-of-project meeting. The final checklist to facilitate the reporting of full systematic review reports contains 54 (sub)items addressing the review's title, abstract, plain language summary, open science, introduction, methods, results, and discussion. Thirteen items pertaining to the title and abstract are also included in a separate abstract checklist, guiding authors in reporting for example conference abstracts. CONCLUSION: PRISMA-COSMIN for OMIs 2024 consists of two checklists (full reports; abstracts), their corresponding explanation and elaboration documents detailing the rationale and examples for each item, and a data flow diagram. PRISMA-COSMIN for OMIs 2024 can improve the reporting of systematic reviews of OMIs, fostering their reproducibility and allowing end-users to appraise the quality of OMIs and select the most appropriate OMI for a specific application. NOTE: This paper was jointly developed by Journal of Clinical Epidemiology, Quality of Life Research, Journal of Patient Reported Outcomes, Health and Quality of Life Outcomes and jointly published by Elsevier Inc, Springer Nature Switzerland AG, and BioMed Central Ltd., part of Springer Nature. The articles are identical except for minor stylistic and spelling differences in keeping with each journal's style. Either citation can be used when citing this article.

Gut Microbiota Dysbiosis and Sleep Disorders: Culprit in Cardiovascular Diseases.

Background: Over the past decade, the gut microbiome (GM) has progressively demonstrated to have a central role in human metabolism, immunity, and cardiometabolic risk. Likewise, sleep disorders showed an impact on individual health and cardiometabolic risk. Recent studies seem to suggest multi-directional relations among GM, diet, sleep, and cardiometabolic risk, though specific interactions are not fully elucidated. We conducted a systematic review to synthesize the currently available evidence on the potential interactions between sleep and GM and their possible implications on cardiometabolic risk. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for reporting systematic reviews and meta-analyses, including articles from January 2016 until November 2022. Narrative syntheses were employed to describe the results. Results: A total of 8 studies were selected according to these criteria. Our findings indicated that the sleep disorder and/or the acute circadian rhythm disturbance caused by sleep-wake shifts affected the human GM, mainly throughout microbial functionality. Conclusions: Sleep disorders should be viewed as cardiovascular risk factors and targeted for preventive intervention. More research and well-designed studies are needed to completely assess the role of sleep deprivation in the multi-directional relationship between GM and cardiometabolic risk.

Assessing the utility of frailty scores in triage: a comparative study of validated scales.

Currently, there is conflicting evidence regarding the efficacy of frailty scales and their ability to enhance or support triage operations. This study aimed to assess the utility of three common frailty scales (CFS, PRISMA-7, ISAR) and determine their utility in the triage setting. This prospective observational monocentric study was conducted at Merano Hospital's Emergency Department (ED) from June 1st to December 31st, 2023. All patients attending this ED during the 80-day study period were included, and frailty scores were correlated with three outcomes: hospitalization, 30-day mortality, and severity of condition as assessed by ED physicians. Patients were categorized by age, and analyses were performed for the entire study population, patients aged 18-64, and those aged 65 or older. Univariate analysis was followed by multivariable analysis to evaluate whether frailty scores were independently associated with the outcomes. In multivariable analysis, none of the frailty scores were found to be associated with the study outcomes, except for the CFS, which was associated with an increased risk of 30-day mortality, with an odds ratio of 1.752 (95% CI 1.148-2.674; p = 0.009) in the general population and 1.708 (95% CI 1.044-2.793; p = 0.033) in the population aged ≥ 65. Presently, available frailty scores do not appear to be useful in the triage context. Future research should consider developing new systems for accurate frailty assessment to support risk prediction in the triage assessment.

Implementation and evaluation of an additional GPT-4-based reviewer in PRISMA-based medical systematic literature reviews.

BACKGROUND: PRISMA-based literature reviews require meticulous scrutiny of extensive textual data by multiple reviewers, which is associated with considerable human effort. OBJECTIVE: To evaluate feasibility and reliability of using GPT-4 API as a complementary reviewer in systematic literature reviews based on the PRISMA framework. METHODOLOGY: A systematic literature review on the role of natural language processing and Large Language Models (LLMs) in automatic patient-trial matching was conducted using human reviewers and an AI-based reviewer (GPT-4 API). A RAG methodology with LangChain integration was used to process full-text articles. Agreement levels between two human reviewers and GPT-4 API for abstract screening and between a single reviewer and GPT-4 API for full-text parameter extraction were evaluated. RESULTS: An almost perfect GPT-human reviewer agreement in the abstract screening process (Cohen's kappa > 0.9) and a lower agreement in the full-text parameter extraction were observed. CONCLUSION: As GPT-4 has performed on a par with human reviewers in abstract screening, we conclude that GPT-4 has an exciting potential of being used as a main screening tool for systematic literature reviews, replacing at least one of the human reviewers.

A systematic review of mathematical models of Lassa fever.

This systematic review, conducted following the PRISMA guidelines, scrutinizes mathematical models employed in the study of Lassa fever. The analysis revealed the inherent heterogeneity in both models and data, posing significant challenges to parameter estimation. While health and behavioral interventions exhibit promise in mitigating the disease's spread, their efficacy is contingent upon contextual factors. Identified through this review are critical gaps, limitations, and avenues for future research, necessitating increased harmonization and standardization in modeling approaches. The considerations of seasonal and spatial variations emerge as crucial elements demanding targeted investigation. The perpetual threat of emerging diseases, coupled with the enduring public health impact of Lassa fever, underscores the imperative for sustained research endeavors and investments in mathematical modeling. The conclusion underscored that while mathematical modeling remains an invaluable tool in the combat against Lassa fever, its optimal utilization mandates multidisciplinary collaboration, refined data collection methodologies, and an enriched understanding of the intricate disease dynamics. This comprehensive approach is essential for effectively reducing the burden of Lassa fever and safeguarding the health of vulnerable populations.

Systematic review and meta-analysis of surgical approaches for improving airway stability in infants with Robin sequence: evaluating complications and outcomes.

The purpose of this systematic review and meta-analysis was to determine the most effective and least morbid surgical technique for relieving retroglossal airway obstruction in infants with Robin sequence (RS). The study adhered to PRISMA guidelines and included 25 studies (24 cohorts and one case series) that investigated interventions for airway improvement, including conservative measures, tongue-lip adhesion (TLA), mandibular distraction osteogenesis (MDO), and tracheostomy. The primary outcome variable was complication rate, while predictor variable was the use of interventions for airway improvement. Results showed that conservative measures were the preferred initial management strategy in most studies, while TLA was recommended for infants with mild obstruction, and MDO or tracheostomy was reserved for severe cases. Only complications could be analysed via meta-analysis due to data heterogeneity, revealing that tracheostomy had a summary odds ratio of 5.39 in favour of TLA, while MDO had a ratio of 2.8 over TLA, and the complication rates were similar between MDO and tracheostomy. If conservative measures fail, the study recommends mandibular distraction as the preferable technique for stable airway improvement. If the infant is unsuitable for distraction, tongue-lip adhesion may serve as an alternative, while tracheostomy should be reserved for cases of severe multi-level obstruction. The authors propose that large-scale, multicentre trials comparing long-term outcomes are required to establish definitive guidelines.

Perception, practices and understanding of teenage pregnancy among the adolescent girls in India: A scoping review protocol.

Introduction: Teenage pregnancy is a subject of concern among adolescents. Inadequate knowledge and misperceptions about pregnancy are major contributing factors to teenage pregnancy. Without a proper understanding, adolescents are involved in unsafe sexual practices, which results in pregnancy. So, perception and understanding are important aspects to explore among adolescents. In this planned scope review, all eligible studies will be identified around the perception, practices, and understanding of teenage pregnancy among married and unmarried adolescent girls. Methods: The Arksey and O'Malley (2005) scoping review framework and the Joanna Briggs Institute Reviewers' Manual (2015) will be used for the planned scoping review. The population, concept, and context strategy (PCC) will be used to develop the research question, search strategy, and eligibility criteria. Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA ScR) will be used for the findings of the study. For the literature search, authors will use Google Scholar, PubMed, and ResearchGate electronic databases with specific words such as "teenage", "adolescence", "pregnancy", "perception", "knowledge", "awareness" and "abortion". Result: The planned scoping review will be helpful in addressing the lack of adolescent misperception, malpractices, and misunderstandings regarding teenage pregnancy. It can provide detailed information about teenage pregnancy in the Indian context. Conclusion: The evidence synthesis and gap analysis will be helpful in suggesting insights into the issue of teenage pregnancy, which will be helpful in future policies and programs.

Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces.

The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets.

Modelling techniques in cholera epidemiology: A systematic and critical review.

Diverse modelling techniques in cholera epidemiology have been developed and used to (1) study its transmission dynamics, (2) predict and manage cholera outbreaks, and (3) assess the impact of various control and mitigation measures. In this study, we carry out a critical and systematic review of various approaches used for modelling the dynamics of cholera. Also, we discuss the strengths and weaknesses of each modelling approach. A systematic search of articles was conducted in Google Scholar, PubMed, Science Direct, and Taylor & Francis. Eligible studies were those concerned with the dynamics of cholera excluding studies focused on models for cholera transmission in animals, socio-economic factors, and genetic & molecular related studies. A total of 476 peer-reviewed articles met the inclusion criteria, with about 40% (32%) of the studies carried out in Asia (Africa). About 52%, 21%, and 9%, of the studies, were based on compartmental (e.g., SIRB), statistical (time series and regression), and spatial (spatiotemporal clustering) models, respectively, while the rest of the analysed studies used other modelling approaches such as network, machine learning and artificial intelligence, Bayesian, and agent-based approaches. Cholera modelling studies that incorporate vector/housefly transmission of the pathogen are scarce and a small portion of researchers (3.99%) considers the estimation of key epidemiological parameters. Vaccination only platform was utilized as a control measure in more than half (58%) of the studies. Research productivity in cholera epidemiological modelling studies have increased in recent years, but authors used diverse range of models. Future models should consider incorporating vector/housefly transmission of the pathogen and on the estimation of key epidemiological parameters for the transmission of cholera dynamics.

A systematic analysis on the efficiency and sustainability of green facades and roofs.

In the modern urban space, green infrastructures have been gaining increasing relevance due to their positive impacts on sustainability issues, visual appeal, and the well-being of individuals. On the other hand, environmental sustainability has become mandatory in the agenda of governments and organizations. Thus, a systematic analysis on the efficiency and sustainability of green facades and roofs spanning key applications, benefits and implementation constraints is welcome. In this paper, we employed the PRISMA method to investigate how these matters were addressed in the recent literature, comprising articles published in scientific journals indexed to the SCOPUS database. Following the web search, selection, systematization, and analysis of that literature, it was revealed that the efficiency of green facades and roofs has been mostly associated with energy and thermal performance in buildings, which brings unequivocal multiple benefits (e.g., consumption savings, mitigation of urban heat island effects) despite of some barriers (e.g., installation and maintenance costs). Other discussions about green facades and roofs involved their valuable roles in stormwater management, considering their retention capacity, and in the treatment of wastewater for reuse in non-potable applications, considering their filtering capacity. It was also discovered the need to improve green infrastructures through the use of cleaner technologies and recycled materials, selection of plants that are appropriate for the local climate, and minimization of construction, transportation, disposal and maintenance costs. Efficiency and sustainability in these cases was prognosed to succeed if the costs were minimized throughout the entire life cycle, and complemented with incentive policies (e.g., tax reduction, agile administrative processes) and collaboration among multidisciplinary teams comprising designers, builders, municipality planners and the academic and market worlds.

Machine Learning Models for Parkinson Disease: Systematic Review.

Background: With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and prediction, including for Parkinson disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world use. In particular, a lack of external validity may result in poor performance of these systems in clinical practice. Additional methodological issues in ML design and reporting can also hinder clinical adoption, even for applications that would benefit from such data-driven systems. Objective: To sample the current ML practices in PD applications, we conducted a systematic review of studies published in 2020 and 2021 that used ML models to diagnose PD or track PD progression. Methods: We conducted a systematic literature review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines in PubMed between January 2020 and April 2021, using the following exact string: "Parkinson's" AND ("ML" OR "prediction" OR "classification" OR "detection" or "artificial intelligence" OR "AI"). The search resulted in 1085 publications. After a search query and review, we found 113 publications that used ML for the classification or regression-based prediction of PD or PD-related symptoms. Results: Only 65.5% (74/113) of studies used a holdout test set to avoid potentially inflated accuracies, and approximately half (25/46, 54%) of the studies without a holdout test set did not state this as a potential concern. Surprisingly, 38.9% (44/113) of studies did not report on how or if models were tuned, and an additional 27.4% (31/113) used ad hoc model tuning, which is generally frowned upon in ML model optimization. Only 15% (17/113) of studies performed direct comparisons of results with other models, severely limiting the interpretation of results. Conclusions: This review highlights the notable limitations of current ML systems and techniques that may contribute to a gap between reported performance in research and the real-life applicability of ML models aiming to detect and predict diseases such as PD.

Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review.

BACKGROUND: Unaddressed early-stage mental health issues, including stress, anxiety, and mild depression, can become a burden for individuals in the long term. Digital phenotyping involves capturing continuous behavioral data via digital smartphone devices to monitor human behavior and can potentially identify milder symptoms before they become serious. OBJECTIVE: This systematic literature review aimed to answer the following questions: (1) what is the evidence of the effectiveness of digital phenotyping using smartphones in identifying behavioral patterns related to stress, anxiety, and mild depression? and (2) in particular, which smartphone sensors are found to be effective, and what are the associated challenges? METHODS: We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process to identify 36 papers (reporting on 40 studies) to assess the key smartphone sensors related to stress, anxiety, and mild depression. We excluded studies conducted with nonadult participants (eg, teenagers and children) and clinical populations, as well as personality measurement and phobia studies. As we focused on the effectiveness of digital phenotyping using smartphones, results related to wearable devices were excluded. RESULTS: We categorized the studies into 3 major groups based on the recruited participants: studies with students enrolled in universities, studies with adults who were unaffiliated to any particular organization, and studies with employees employed in an organization. The study length varied from 10 days to 3 years. A range of passive sensors were used in the studies, including GPS, Bluetooth, accelerometer, microphone, illuminance, gyroscope, and Wi-Fi. These were used to assess locations visited; mobility; speech patterns; phone use, such as screen checking; time spent in bed; physical activity; sleep; and aspects of social interactions, such as the number of interactions and response time. Of the 40 included studies, 31 (78%) used machine learning models for prediction; most others (n=8, 20%) used descriptive statistics. Students and adults who experienced stress, anxiety, or depression visited fewer locations, were more sedentary, had irregular sleep, and accrued increased phone use. In contrast to students and adults, less mobility was seen as positive for employees because less mobility in workplaces was associated with higher performance. Overall, travel, physical activity, sleep, social interaction, and phone use were related to stress, anxiety, and mild depression. CONCLUSIONS: This study focused on understanding whether smartphone sensors can be effectively used to detect behavioral patterns associated with stress, anxiety, and mild depression in nonclinical participants. The reviewed studies provided evidence that smartphone sensors are effective in identifying behavioral patterns associated with stress, anxiety, and mild depression.

The rise in popularity of central bank digital currencies. A systematic review.

Central bank digital currencies (CBDCs) have been growing in popularity since 2018, as worldwide countries explore their impact and implementation options. This article analyzes the state of research around central bank digital currencies and the evolving landscape of CBDCs, and explores emerging areas of research and trends by using the PRISMA method and VOSviewer, with the goal of showing the main opportunities and challenges related to them. AMSTAR, DistillerSR, Eppi-Reviewer, ROBIS, and SRDR were the screening and quality evaluation tools employed for study eligibility criteria, design screening and content selection, text analysis data extraction, methodological quality predictors, and reliable and reproducible evidence assessment. A total of 1024 articles on central bank digital currencies were identified in Scopus and the Web of Science, out of which 747 have been included in the review (documents which were not in English language and not categorized as journal articles were excluded). Through an analysis of the relevant literature, the study categorizes CBDC research into positive, negative and neutral research, with a particular focus on sustainability issues, and conducts a keyword co-occurrence analysis using VOSviewer, following a narrowing down of the relevant articles to be included in the study by applying the PRISMA framework. This generates an overall view for experts and researchers who can use the main analyzed features of CBDCs and adapt them accordingly, taking into account relevant macroeconomic characteristics. The study highlights the need to continue interdisciplinary research, by adapting the research and CBDC characteristics to keep up with the latest technologies and with the shift towards green finance, and explores the elaborate relationship between finance, technology and sustainability.

National Public Health Dashboards: Protocol for a Scoping Review.

BACKGROUND: The COVID-19 pandemic highlighted the importance of robust public health data systems and the potential utility of data dashboards for ensuring access to critical public health data for diverse groups of stakeholders and decision makers. As dashboards are becoming ubiquitous, it is imperative to consider how they may be best integrated with public health data systems and the decision-making routines of diverse audiences. However, additional progress on the continued development, improvement, and sustainability of these tools requires the integration and synthesis of a largely fragmented scholarship regarding the purpose, design principles and features, successful implementation, and decision-making supports provided by effective public health data dashboards across diverse users and applications. OBJECTIVE: This scoping review aims to provide a descriptive and thematic overview of national public health data dashboards including their purpose, intended audiences, health topics, design elements, impact, and underlying mechanisms of use and usefulness of these tools in decision-making processes. It seeks to identify gaps in the current literature on the topic and provide the first-of-its-kind systematic treatment of actionability as a critical design element of public health data dashboards. METHODS: The scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The review considers English-language, peer-reviewed journal papers, conference proceedings, book chapters, and reports that describe the design, implementation, and evaluation of a public health dashboard published between 2000 and 2023. The search strategy covers scholarly databases (CINAHL, PubMed, Medline, and Web of Science) and gray literature sources and uses snowballing techniques. An iterative process of testing for and improving intercoder reliability was implemented to ensure that coders are properly trained to screen documents according to the inclusion criteria prior to beginning the full review of relevant papers. RESULTS: The search process initially identified 2544 documents, including papers located via databases, gray literature searching, and snowballing. Following the removal of duplicate documents (n=1416), nonrelevant items (n=839), and items classified as literature reviews and background information (n=73), 216 documents met the inclusion criteria: US case studies (n=90) and non-US case studies (n=126). Data extraction will focus on key variables, including public health data characteristics; dashboard design elements and functionalities; intended users, usability, logistics, and operation; and indicators of usefulness and impact reported. CONCLUSIONS: The scoping review will analyze the goals, design, use, usefulness, and impact of public health data dashboards. The review will also inform the continued development and improvement of these tools by analyzing and synthesizing current practices and lessons emerging from the literature on the topic and proposing a theory-grounded and evidence-informed framework for designing, implementing, and evaluating public health data dashboards. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52843.

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.