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1.
Sensors (Basel) ; 23(2)2023 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-36679408

RESUMO

Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.


Assuntos
Crowdsourcing , Humanos , Crowdsourcing/métodos , Incerteza , Aprendizagem , Adaptação Fisiológica
2.
Ann Fam Med ; 21(1): 73-75, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36690496

RESUMO

Some patients develop multiple protracted sequelae after infection with SARS-CoV-2, collectively known as post-COVID syndrome or long COVID. To date, there is no evidence showing benefit of specific therapies for this condition, and patients likely resort to self-initiated therapies. We aimed to obtain information about therapies used by and needs of this population via inductive crowdsourcing research. Patients completed an online questionnaire about their symptoms and experiences with therapeutic approaches. Responses of 499 participants suggested few approaches (eg, mind-body medicine, respiratory therapy) had positive effects and showed a great need for patient-centered communication (eg, more recognition of this syndrome). Our findings can help design clinical studies and underscore the importance of the holistic approach to care provided by family medicine.


Assuntos
COVID-19 , Crowdsourcing , Humanos , SARS-CoV-2 , Comunicação
3.
Simul Healthc ; 18(1): 71-72, 2023 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-36716005

RESUMO

The purpose of this report is to: (1) highlight challenges of transitioning the delivery of simulation from centralized, in-person laboratory to decentralized, home-based, online format; (2) suggest a solution that involves the use of crowdsourcing community-based 3-dimensional printers to produce affordable simulators; and (3) present exploratory research and a test case aiming to identify crowdsourcing frameworks to accomplish this. We present a test case that shows the potential of the proposed solution to scale up the decentralized simulation practices during and beyond the COVID-19 pandemic. As a largely uncharted territory, the test case highlighted successes and areas for improvement that need to be addressed through both theoretical and empirical research and testing before full implementation and scale-up.


Assuntos
COVID-19 , Crowdsourcing , Humanos , Crowdsourcing/métodos , Pandemias , Simulação por Computador
4.
Sci Rep ; 12(1): 20724, 2022 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-36456578

RESUMO

Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide events in newspapers or crowdsourcing platforms allows fast observation, surveying and classification. Currently, few studies have been produced on the combination of social media data and traditional sensors. This gap indicates that it is unclear how their integration can effectively provide emergency managers with appropriate knowledge. In this work, rainfall, human lives, and earmarked fund data sources were correlated to "landslide news". Analysis was applied to obtain information about temporal (2010-2019) and spatial (regional and warning hydrological zone scale) distribution. The temporal distribution of the data shows a continuous increase from 2015 until 2019 for both landslide and rainfall events. The number of people involved and the amount of earmarked funds do not exhibit any clear trend. The spatial distribution displays good correlation between "landslide news", traditional sensors (e.g., pluviometers) and possible effects in term of fatalities. In addition, the cost of soil protection, in monetary terms, indicates the effects of events.


Assuntos
Crowdsourcing , Desastres , Deslizamentos de Terra , Humanos , Itália , Hidrologia
6.
Sci Rep ; 12(1): 21990, 2022 12 20.
Artigo em Inglês | MEDLINE | ID: mdl-36539519

RESUMO

Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.


Assuntos
COVID-19 , Crowdsourcing , Humanos , COVID-19/diagnóstico , SARS-CoV-2 , Tosse/diagnóstico , Pandemias , Reprodutibilidade dos Testes , Reação em Cadeia da Polimerase em Tempo Real , Medidas de Resultados Relatados pelo Paciente
7.
J Med Syst ; 46(12): 101, 2022 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-36418791

RESUMO

Unfortunately, many of the diabetes mobile apps have operational and design flaws that are debarring users from maximizing from the self-management paradigm. We, therefore, aim to identify the markers of operational and design flaws of diabetes mobile apps to facilitate a better user-centred design. e crowdsourced negative user review comments (rating score: 1-3) of 47 diabetes mobile apps from the google play store. A total of 781 negative user comments (rating score 1-3) from the apps are coded to identify and categorize the themes relating to the operational and design flaws. The operational and design flaws account for 50.32% of the challenges faced by the unhappy diabetes mobile apps users. Among them, 44.73% have issues with app crashing, 17.3% are concerned about device compatibility that inhibits seamless operations, 9.67% are worried about the problem of data uploading. Poor design is a worry to 19.29% of the users who complain of the crowded user interface, poor data management, poor analytics, difficulty scheduling doctors' appointments, and transferring data. More patients with diabetes can be encouraged to continue using diabetes mobile apps for self-management of diabetes through improved design and a pace-wise software advancement to match the ever-growing enhancements in android operating systems and telecommunication devices. This will help to counter most of the challenges identified in this study.


Assuntos
Crowdsourcing , Diabetes Mellitus , Aplicativos Móveis , Autogestão , Humanos , Diabetes Mellitus/terapia , Agendamento de Consultas
8.
PLoS One ; 17(11): e0277223, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36350898

RESUMO

Credibly estimating social-ecological relationships requires data with broad coverage and fine geographic resolutions that are not typically available from standard ecological surveys. Open and unstructured data from crowdsourced platforms offer an opportunity for collecting large quantities of user-submitted ecological data. However, the representativeness of the areas sampled by these data portals is not well known. We investigate how data availability in eBird, one of the largest and most popular crowdsourced science platforms, correlates with race and income of census tracts in two cities: Boston, MA and Phoenix, AZ. We find that checklist submissions vary greatly across census tracts, with similar patterns within both metropolitan regions. In particular, census tracts with high income and high proportions of white residents are most likely to be represented in the data in both cities, which indicates selection bias in eBird coverage. Our results illustrate the non-representativeness of eBird data, and they also raise deeper questions about the validity of statistical inferences regarding disparities that can be drawn from such datasets. We discuss these challenges and illustrate how sample selection problems in unstructured or semi-structured crowdsourced data can lead to spurious conclusions regarding the relationships between race, income, and access to urban bird biodiversity. While crowdsourced data are indispensable and complementary to more traditional approaches for collecting ecological data, we conclude that unstructured or semi-structured data may not be well-suited for all lines of inquiry, particularly those requiring consistent data coverage, and should thus be handled with appropriate care.


Assuntos
Crowdsourcing , Biodiversidade , Cidades , Meio Social , Boston
9.
PLoS One ; 17(11): e0275898, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36367868

RESUMO

Amidst the Coronavirus crisis, many fundraising projects have emerged to relieve financial burdens resulting from social distancing policies. Crowdfunding is a way to raise money to fund a business, project or charity, through either social media or other online platforms to reach hundreds of potential sponsors. We developed guidelines for effective donation-based crowdfunding through online platforms. Using Futures Research (FR) technique, we conducted our analyses in 3 phases. In Phase 1, we reviewed relevant literature and conducted in-depth interviews of related parties. In Phase 2, we interviewed experts using Ethnographic Futures Research (EFR) technique. In Phase 3, we visualized the future using the principles of Futures Wheel, Cross-impact Matrix and Scenarios. Based on our findings, effective donation-based crowdfunding platforms should adopt Blockchain technology for transparency and accountability, and incentivize donations to keep backers loyal. Founders should be required to obtain fundraising licenses from relevant regulators. Finally, laws and regulations that protect platform users should be standardized internationally. Our proposed guidelines hope to improve the quality and transparency of future fundraising activities.


Assuntos
Crowdsourcing , Administração Financeira , Obtenção de Fundos , Mídias Sociais , Humanos , Crowdsourcing/métodos
10.
Am J Health Behav ; 46(5): 497-502, 2022 Oct 17.
Artigo em Inglês | MEDLINE | ID: mdl-36333833

RESUMO

OBJECTIVE: In this study, we examined the impact of a range of methods to improve data quality on the demographic and health status representativeness of Amazon Mechanical Turk (MTurk) samples. METHODS: We developed and field-tested a general survey of health on MTurk in 2017 among 5755 participants and 2021 among 6752 participants. We collected information on participant demographic characteristics and health status and implemented different quality checks in 2017 and 2021. RESULTS: Adding data quality checks generally improves the representativeness of the final MTurk sample, but there are persistent differences in mental health and pain conditions, age, education, and income between the MTurk population and the broader US population. CONCLUSION: We conclude that data quality checks improve the data quality and representativeness.


Assuntos
Crowdsourcing , Humanos , Inquéritos e Questionários , Nível de Saúde
11.
Sensors (Basel) ; 22(22)2022 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-36433428

RESUMO

Noise has become a very notable source of pollution with major impacts on health, especially in urban areas. To reduce these impacts, proper evaluation of noise is very important, for example by using noise mapping tools. The Noise-Planet project seeks to develop such tools in an open science platform, with a key open-source smartphone tool "NoiseCapture" that allows users to measure and share the noise environment as an alternative to classical methods, such as simulation tools and noise observatories, which have limitations. As an alternative solution, smartphones can be used to create a low-cost network of sensors to collect the necessary data to generate a noise map. Nevertheless, this data may suffer from problems, such as a lack of calibration or a bad location, which lowers its quality. Therefore, quality control is very crucial to enhance the data analysis and the relevance of the noise maps. Most quality control methods require a reference database to train the models. In the context of NC, this reference data can be produced during specifically organized events (NC party), during which contributors are specifically trained to collect measurements. Nevertheless, these data are not sufficient in number to create a big enough reference database, and it is still necessary to complete them. Other communities around the world use NC, and one may want to integrate the data they collected into the learning database. In order to achieve this, one must detect these data within the mass of available data. As these events are generally characterized by a higher density of measurements in space and time, in this paper we propose to apply a classical clustering method, called DBSCAN, to identify them in the NC database. We first tested this method on the existing NC party, then applied it on a global scale. Depending on the DBSCAN parameters, many clusters are thus detected, with different typologies.


Assuntos
Crowdsourcing , Smartphone , Análise por Conglomerados , Bases de Dados Factuais , Análise de Dados
12.
Sensors (Basel) ; 22(19)2022 Oct 10.
Artigo em Inglês | MEDLINE | ID: mdl-36236775

RESUMO

Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that crowdfunding scams are a new imminent threat to investors, little is known about them primarily due to the lack of measurement data collected from real scam cases. This paper fills the gap by collecting, labeling, and analyzing publicly available data of a hundred fraudulent campaigns on a crowdfunding platform. In order to find and understand distinguishing characteristics of crowdfunding scams, we propose to use a broad range of traits including project-based traits, project creator-based ones, and content-based ones such as linguistic cues and Named Entity Recognition features, etc. We then propose to use the feature selection method called Forward Stepwise Logistic Regression, through which 17 key discriminating features (including six original and hitherto unused ones) of scam campaigns are discovered. Based on the selected 17 key features, we present and discuss our findings and insights on distinguishing characteristics of crowdfunding scams, and build our scam detection model with 87.3% accuracy. We also explore the feasibility of early scam detection, building a model with 70.2% of classification accuracy right at the time of project launch. We discuss what features from which sections are more helpful for early scam detection on day 0 and thereafter.


Assuntos
Crowdsourcing , Crowdsourcing/métodos
13.
Elife ; 112022 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-36269056

RESUMO

The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter ß, whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates.


During the COVID-19 pandemic, public health officials promoted social distancing as a way to reduce SARS-CoV-2 transmission. The goal of social distancing is to reduce the number, proximity, and duration of face-to-face interactions between people. To achieve this, people shifted many activities online or canceled events outright. In education, some schools closed and shifted to online learning, while others continued classes in person with safety precautions. Better information about SARS-CoV-2 transmission in schools could help public health officials to make decisions of what activities to keep in person and when to suspend classes. If safety measures lower transmission in schools considerably, then closing schools may not be worth online education's social, educational, and economic costs. However, if transmission of SARS-CoV-2 in schools remains high despite measures, closing schools may be essential, despite the costs. Tupper et al. used data about COVID-19 cases in children attending in-person school in four Canadian provinces between 2020 and 2021 to fit a computer model of school transmission. On average, their analysis shows that one infected person in a school leads to between two and three further cases. Most of the time, no more students are infected, indicating that normally infection clusters are small; and only rarely does one infected person set off a large outbreak. The model also showed that measures to reduce transmission, like masking or small class sizes, were more effective than interventions such as keeping students with the same cohort all day (bubbling). Tupper et al. caution that their findings apply to the variants of SARS-CoV-2 circulating in Canada during the 2020-2021 school year, and may not apply to newer, highly transmissible strains like Omicron. However, the model could always be adapted to assess school or workplace transmission of more recent strains of SARS-CoV-2, and more generally of other diseases. Thus, Tupper et al. provide a new approach to estimating the rate of disease transmission and comparing the impact of different prevention strategies.


Assuntos
COVID-19 , Crowdsourcing , Humanos , COVID-19/epidemiologia , SARS-CoV-2 , Canadá/epidemiologia , Instituições Acadêmicas
14.
J Med Internet Res ; 24(10): e38963, 2022 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-36264627

RESUMO

BACKGROUND: Problematic smartphone use, like problematic internet use, is a condition for which treatment is being sought on the web. In the absence of established treatments, smartphone-provided tools that monitor or control smartphone use have become increasingly popular, and their dissemination has largely occurred without oversight from the mental health field. OBJECTIVE: We aimed to assess the popularity and perceived effectiveness of smartphone tools that track and limit smartphone use. We also aimed to explore how a set of variables related to mental health, smartphone use, and smartphone addiction may influence the use of these tools. METHODS: First, we conducted a web-based survey in a representative sample of 1989 US-based adults using the crowdsourcing platform Prolific. Second, we used machine learning and other statistical tools to identify latent user classes; the association between latent class membership and demographic variables; and any predictors of latent class membership from covariates such as daily average smartphone use, social problems from smartphone use, smartphone addiction, and other psychiatric conditions. RESULTS: Smartphone tools that monitor and control smartphone use were popular among participants, including parents targeting their children; for example, over two-thirds of the participants used sleep-related tools. Among those who tried a tool, the highest rate of perceived effectiveness was 33.1% (58/175). Participants who experienced problematic smartphone use were more likely to be younger and more likely to be female. Finally, 3 latent user classes were uncovered: nonusers, effective users, and ineffective users. Android operating system users were more likely to be nonusers, whereas younger adults and females were more likely to be effective users. The presence of psychiatric symptoms did not discourage smartphone tool use. CONCLUSIONS: If proven effective, tools that monitor and control smartphone use are likely to be broadly embraced. Our results portend well for the acceptability of mobile interventions in the treatment of smartphone-related psychopathologies and, potentially, non-smartphone-related psychopathologies. Better tools, targeted marketing, and inclusive design, as well as formal efficacy trials, are required to realize their potential.


Assuntos
Crowdsourcing , Smartphone , Adulto , Criança , Feminino , Humanos , Masculino , Inquéritos e Questionários , Transtorno de Adição à Internet , Aprendizado de Máquina
15.
Sci Rep ; 12(1): 16937, 2022 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-36209288

RESUMO

We propose a multi-agent learning approach for designing crowdsourcing contests and All-Pay auctions. Prizes in contests incentivise contestants to expend effort on their entries, with different prize allocations resulting in different incentives and bidding behaviors. In contrast to auctions designed manually by economists, our method searches the possible design space using a simulation of the multi-agent learning process, and can thus handle settings where a game-theoretic equilibrium analysis is not tractable. Our method simulates agent learning in contests and evaluates the utility of the resulting outcome for the auctioneer. Given a large contest design space, we assess through simulation many possible contest designs within the space, and fit a neural network to predict outcomes for previously untested contest designs. Finally, we apply mirror ascent to optimize the design so as to achieve more desirable outcomes. Our empirical analysis shows our approach closely matches the optimal outcomes in settings where the equilibrium is known, and can produce high quality designs in settings where the equilibrium strategies are not solvable analytically.


Assuntos
Crowdsourcing , Aprendizado Profundo , Simulação por Computador , Motivação
16.
Nature ; 609(7927): 641-643, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-36097058
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 3418-3421, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36085800

RESUMO

We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence. To increase the model's capacity to identify a minority class, data augmentation and cost-sensitive loss are incorporated (infected samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50 has been found to be effective. The unified framework also integrates deep ensemble learning and uncertainty estimation to integrate predictions from various base classifiers for generalisation and reliability. We ran a series of tests using the DiCOVA2021 challenge dataset to assess the efficacy of our proposed method, and the results show that our method has an AUC-ROC of 85.43 percent, making it a promising method for COVID-19 detection. The unified framework also demonstrates that audio may be used to quickly diagnose different respiratory disorders.


Assuntos
COVID-19 , Crowdsourcing , COVID-19/diagnóstico , Tosse/diagnóstico , Humanos , Reprodutibilidade dos Testes , Incerteza
18.
Comput Intell Neurosci ; 2022: 7814550, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36072753

RESUMO

As the fastest-growing crowdfunding model, equity crowdfunding (ECF) brings high returns and uncertainty. In this context, it is crucial to understand these crowdfunding projects' actual performance. Since ECF is currently in the early stage of integration, there are still a lot of risk issues, such as the uncertainty of equity structure, capital supervision, or project management. Therefore, this paper develops a new profitability indicator, "return on registered capital," to test its impact on the ECF project's actual return. This paper studies which factors affect the financial performance of ECF projects through the traditional statistical model and a deep neural network (DNN) model. There is evidence that return on registered capital affects the actual return of the project. At the same time, the company's operating time and the number of employees had an unexpected effect on project performance. In addition, the recognition accuracy of the DNN model in this study exceeds 97%, which affirms the applicability of the DNN model in the analysis of ECF success factors. This paper also uses tenfold cross-validation to prove that deep learning has certain advantages in this topic's accuracy and generalization error. This study explores whether company representatives' gender and knowledge level affect project performance. The results will be described in detail in the paper.


Assuntos
Crowdsourcing , China , Crowdsourcing/economia , Humanos
19.
Artigo em Inglês | MEDLINE | ID: mdl-36078514

RESUMO

The low-carbon city has become an important global urban development-oriented goal. One important aspect of urban space is low-carbon urban planning, which has a vital role in urban carbon emissions. Which types of urban form and function allocations are conducive to reducing carbon emissions is therefore a key issue. In this study, the Futian and Luohu Districts of Shenzhen, Guangdong Province, China, are taken as an example to investigate this issue. Firstly, a "head/tail" breaks method based on the third fractal theory is adopted to obtain the minimum evaluation parcel of urban space. Then, the Landscape Shape Index (LSI), Fragmentation Index (C), Shannon's Diversity Index (SHDI), and Density of Public Facilities (Den) are used to evaluate the form and function allocation of each parcel. In addition, the CO2 concentration distribution in this study area is acquired from remote sensing satellite data. Finally, the relationships between urban form, function allocation, and CO2 concentration are obtained. The results show that the lower the urban form index or the higher the urban function index, the less the CO2 concentration. To verify this conclusion, three experiments are designed and carried out. In experiment A, the CO2 concentration of the tested area is reduced by 14.31% by decreasing the LSI and C by 6.1% and 9.4%, respectively. In experiment B, the CO2 concentration is reduced by 15.15% by increasing the SHDI and Den by 16.3% and 12.1%, respectively. In experiment C, the CO2 concentration is reduced by 27.72% when the urban form and function are adjusted in the same was as in experiments A and B.


Assuntos
Carbono , Crowdsourcing , Carbono/análise , Dióxido de Carbono/análise , China , Cidades , Planejamento de Cidades
20.
BMC Public Health ; 22(1): 1697, 2022 09 07.
Artigo em Inglês | MEDLINE | ID: mdl-36071401

RESUMO

BACKGROUND: Adherent pre-exposure prophylaxis (PrEP) uptake can prevent HIV infections. Despite the high HIV incidence, Chinese key populations have low PrEP uptake and adherence. New interventions are needed to increase PrEP adherence among key populations in China. Co-creation methods are helpful to solicit ideas from the community to solve public health problems. The study protocol aims to describe the design of a stepped-wedge trial and to evaluate the efficacy of co-created interventions to facilitate PrEP adherence among key populations in China. METHODS: The study will develop intervention packages to facilitate PrEP adherence among Chinese key populations using co-creation methods. The study will then evaluate the efficacy of the co-created intervention packages using a stepped-wedge randomized controlled trial. This four-phased closed cohort stepped-wedge design will have four clusters. Each cluster will start intervention at three-month intervals. Seven hundred participants who initiated PrEP will be recruited. Participants will be randomized to the clusters using block randomization. The intervention condition includes receiving co-created interventions in addition to standard of care. The control condition is the standard of care that includes routine clinical assessment every 3 months. All participants will also receive an online follow-up survey every 3 months to record medication adherence and will be encouraged to use a WeChat mini-app for sexual and mental health education throughout the study. The primary outcomes are PrEP adherence and retention in PrEP care throughout the study period. We will examine a hypothesis that a co-created intervention can facilitate PrEP adherence. Generalized linear mixed models will be used for the primary outcome analysis. DISCUSSION: Developing PrEP adherence interventions in China faces barriers including suboptimal PrEP uptake among key populations, the lack of effective PrEP service delivery models, and insufficient community engagement in PrEP initiatives. Our study design addresses these obstacles by using co-creation to generate social media-based intervention materials and embedding the study design in the local healthcare system. The study outcomes may have implications for policy and intervention practices among CBOs and the medical system to facilitate PrEP adherence among key populations. TRIAL REGISTRATION: The study is registered in Clinical Trial databases in China (ChiCTR2100048981, July 19, 2021) and the US (NCT04754139, February 11, 2021).


Assuntos
Crowdsourcing , Infecções por HIV , Profilaxia Pré-Exposição , China , Infecções por HIV/epidemiologia , Humanos , Adesão à Medicação , Profilaxia Pré-Exposição/métodos , Ensaios Clínicos Controlados Aleatórios como Assunto
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