Willingness Of Users Research Articles

Characteristics associated with willingness to walk further than necessary to the bus stop: Insights for public transport-related physical activity

Background and aimsThere is untapped potential for public transport-related physical activity to make an important contribution to total physical activity and hence improve health. This study aimed to determine the willingness of public transport users to walk further than necessary to a stop, and the characteristics of those willing to do so. MethodsThis study used data from three waves (2016, 2017, 2018) of the Metro Tasmania Customer Satisfaction Survey (CSS), an annual cross-sectional survey of bus passengers in Tasmania, Australia. Linear and logistic regression modelled associations between socio-demographic and transport behaviour characteristics with willingness to travel further to usual bus stop if frequency was improved (yes/no), and if willing, the extra distance (metres) prepared to travel. Analyses were restricted to those reporting walking to their bus stop (n = 1402). ResultsHalf of 1402 bus passengers were prepared to walk further (average 521 m) to their usual bus stop if frequency was improved. A range of characteristics were positively (residential location) and negatively (age, female sex, retired/on pension, no regular motor vehicle access, intermittent public transport use, higher public transport-related physical activity) associated with willingness to walk further to the bus stop. Distance willing to walk varied by a range of demographic characteristics (sex, household income, age, employment status, bus use, public transport-related physical activity). Only age was consistently associated across all three surveys: younger bus passengers were more willing to walk further and recorded greater distances willing to walk. ConclusionsThe characteristics identified provide insights into which population groups may be more amenable to strategies to increase public transport-related physical activity, and which groups may require further investigation to identify reasons for lack of willingness to walk further to bus stops. Improving bus service frequency may increase public transport-related physical activity and confer health benefits.

Factors Affecting Portal Usage Among Chronically Ill Patients During the COVID-19 Pandemic in the Netherlands: Cross-sectional Study.

BackgroundThe COVID-19 pandemic has impacted the capacity of the regular health care system, which is reflected in limited access to nonurgent care for patients who are chronically ill in the Dutch health care system. Nevertheless, many of them still depend on health care assistance to manage their illnesses. Patient portals are used to provide continued health care (remotely) and offer self-management tools during COVID-19 and potentially after. However, little is known about the factors influencing portal use and users’ satisfaction among patients who are chronically ill during the COVID-19 pandemic.ObjectiveThis study aims to examine predictors of patient portal use among patients who are chronically ill, the willingness to recommend the portal to others, and the likelihood of future use among portal nonusers.MethodsAn online self-administered questionnaire was distributed among patients who are chronically ill via social media in May 2020. The questionnaire consisted of four parts: (1) demographics including age and hours of daily internet use; (2) physical health status including COVID-19 infection, perceived level of control, and hospital visits; (3) mental health status including depression and life satisfaction; and (4) portal use including response waiting time and awareness. Descriptive, correlation, univariate, and multivariate analyses were conducted to identify factors that affect portal use, users’ willingness to recommend, and nonusers’ likelihood of future portal use.ResultsA total of 652 patients responded, and 461 valid questionnaires were included. Among the 461 patients, 67% (n=307) were identified as patient portal users. Of the nonusers, 55% (85/154) reported not being aware of the existence of a patient portal at their hospital. Significant predictors of portal use include level of control (P=.04), hospital visit time (P=.03), depression scale (P=.03), and status of life satisfaction (P=.02). Among portal users, waiting time to get a response via the portal (P<.001) and maximum acceptable waiting time (P<.001) were the strongest predictors for willingness to recommend the portal; among nonusers, the model predicted that those who were not aware of patient portals (P<.001) and were willing to wait moderately long (P<.001) were most likely to use the portal in the future.ConclusionsThis study provides insights into factors that influence portal use and willingness to recommend, based on which health care providers can improve the adoption of patient portals and their services. It suggests that health care providers should leverage efficient operations management to improve responsiveness and reduce waiting time to enhance user satisfaction and willingness to recommend use. Health care organizations need to increase portal awareness among nonusers and train their patients to increase both use and longer adoption of patient portals. Factors including depression and life satisfaction can influence portal use; therefore, future studies on determinants of portal use and nonuse in this specific population are needed.

Cyber-Identity Theft and Fintech Services

The fear of identity theft has been considered as an impediment to the rapid utilization of financial technology (FinTech) services. Based on the technology threat avoidance theory (TTAT), this study aims at investigating the willingness of users to use FinTech services notwithstanding the increasing rate of identity theft. This study examines the relationship between the constructs of fear of financial loss, fear of reputational damage, intention to use, and the actual use of FinTech services. Using multiple linear regression models and Pearson's correlation, the results indicate that the fear of financial loss and the fear of reputational damage have no statistical significant relationship with the intention to use and the use of FinTech services. However, the results show a significant relationship between intention to use and the actual use of FinTech services. Consequently, the study indicates that the fear of identity theft does not significantly deter users from using FinTech services.

Evaluating Multitimescale Response Capability of EV Aggregator Considering Users’ Willingness

With the wide deployment of charging piles and the development of vehicle-to-grid technology, electric vehicles (EVs) will have more opportunities to participate in the operation and scheduling of electric power system through the EV aggregator (EVA), an intermediate agent between the power grid and EV users. Quantitatively evaluating the response capability (RC) of EVA, i.e., charging and discharging power ranges, is of fundamental importance for its trading with the operator of power grid. This article proposes a user-aware method to obtain a more accurate range of multitimescale RC considering the response willingness of EV users. First, a temporal RC evaluation model of a single EV considering charge-discharge state and state of charge (SOC) is established. Second, based on the user psychology model, which reflects the relationship between users' responsivity and incentive price, a multitimescale RC evaluation model of EVA considering users' willingness is built. The day-ahead RC of EVA is evaluated by the state prediction data of EVs. According to the control strategy which considers response time and SOC indicators comprehensively, the RC evaluation is updated intraday. Finally, using the statistical data from the EU MERGE project, the effectiveness of the proposed evaluation model is verified, and the impact of incentive price and scheduling time scale on the RC of EVA are analyzed. The results indicate that the proposed model can effectively track the scheduling goals of the system and realize the dynamic update of the RC of EVA.

Abstract 248: A novel PubMed visualizer using human-computer interaction technology: EEEvis.com

Abstract PubMed is the most widely used database and search-engine in biomedical and healthcare field. It has evolved more powerfully using Best Match algorithm. However, users still have several difficulties to ‘catch' their target papers in limited time, which fundamentally originates from the conventional two-dimensional ‘top-down' display method of search results. We conducted a survey for 76 medical experts (including oncologists, surgeons, gastroenterologists, psychiatrists, et al) about their behaviors and difficulties in using PubMed. The most common ‘unmet needs' of users were (1) the rank of citation counts or impact factors, (2) more three-dimensional and interactive display of search results, and (3) author rank and network. Reflecting the results above, we developed a novel PubMed visualizing program, EEEvis version 1.0 (medical and healthcare evidence visualizer), and launched it on www.EEEvis.com. The basic operating principle of EEEvis version 1.0 is to visualize the metadata of PubMed and PubTator (a web-based text mining-tool) using (1) advanced filter tools, (2) a scatter &amp; box plot using citation counts over years, (3) an author map reflecting the ranks and networks, and (4) a reinforced search list (Table 1). All the data crawling is based on the API (application programming interface) of PubMed and PubTator. To computing and visualizing the crawled metadata, we constructed two servers; a hidden station server and an open UI (user interface) server. In the interim result of our ongoing pilot study in searching oncology-related literatures, the two quantitative factors including (1) time to ‘catch' the target paper and (2) success rate finding the required paper showed significant improvement. Other qualitative factors including (3) user convenience, (4) interactivity, and (5) willingness to use EEEvis next time showed good results. Now we are developing EEEvis version 2.0 using text-mining technology and applying multiple patents for this program. Comparison between EEEvis and PubMedProgramsEEEvisPubMedFilter sectionBy article typeYesYesBy publication yearYesYesBy citation count of paperYesNoBy impact factor of journalYesNoInteraction section using Brushing &amp; Linking techniqueScatter plot of citation counts by yearYesNo(with box plot of citation counts by year)YesNoMap of author networkYesNoList sectionSort by best matchYesYesSort by most recentYesYesSort by citation countYesNoSort by author or journalNoYesCitation count of each paperYesNoImpact factor of each journalYesNoPubTator informationYesNoData crawlingHow to crawl results from Pubmed serverIndirect using APIDirectLimit of result numbers10,000 (best matched)NoneUser experience in searching cancer-related keywords in pilot study (ongoing)Time to display results after entering keywordsNot inferiorPromptTime from visualizing results to finding the required paperSuperior(= faster)InferiorSuccess rate of finding the required paper after visualizing the resultsSuperiorInferior Citation Format: Jong-chan Lee, Brian J. Lee, Hyunjoo Song, Changhee Park, Chan-Young Ock, Yuna Youn, Hyojae Sung, Sungjin Woo, Jaihwan Kim, Jin-Hyeok Hwang. A novel PubMed visualizer using human-computer interaction technology: EEEvis.com [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 248.

The Sustainability of Knowledge-Sharing Behavior Based on the Theory of Planned Behavior in Q&amp;A Social Network Community

With the update and iteration of Internet technology, the socialized Q&amp;A (question-and-answer) platform realizes the cross-border dissemination of knowledge with the main purpose of disseminating and sharing knowledge. Zhihu, as a knowledge-sharing platform that relies on the user-generated content model to maintain operation and the strong willingness of users to share knowledge, plays a key role in the development of the community. Currently, social Q&amp;A platforms are facing problems such as low user participation rate and gradual decrease in the number of active users. It is very important and urgent to explore the factors that affect users’ willingness to share knowledge. In response to this problem, this paper builds a theoretical model of the factors that are affecting users’ willingness to share knowledge and uses questionnaire research methods to conduct research design and collect sample data and uses methods such as correlation analysis and structural equation modeling to verify the model and hypothesis. The research results show that the theoretical model of planned behavior has strong explanatory power and self-efficacy and material rewards have a positive effect on knowledge-sharing attitudes. Finally, according to some research results, this paper shows that, with the change of time, young people have different needs for knowledge sharing than before, while realizing self-worth through sharing experience, and we also hope to protect own interests and hope that there are more factors to encourage more users to share their knowledge and experience. Therefore, we propose that the platform can use incentive mechanisms to promote knowledge sharing while helping sharers realize their self-worth, improve the existing functions of the platform, or carry out activities to encourage users to participate, so as to achieve the purpose of knowledge sharing and maintain the operation of the Zhihu platform.

Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System

The present study aimed to use the proposed system to measure and analyze brain waves of users to allow intelligent upper limb rehabilitation and to optimize the system using a genetic algorithm. The study used EPOC Neuroheadset for Emotiv with EEG electrodes attached as a non-invasive method for measuring brain waves. The brain waves were measured according to the EEG 10-20 standard electrode layout, which allows measurement of signals from each spot where electrodes are attached based on EEG characteristics. The measured data were added in a database. In the intelligent neuro-fuzzy model, wave transform was used for extracting brain wave characteristics according to user intentions and to eliminate noise from the signals in an effort to increase reliability. Moreover, to construct the option rules of the neuro-fuzzy system, FCM technique and optimal cluster evaluation method were used. Furthermore, the asymmetric Gaussian membership function was used to improve performance, whereas SD and WF divided into left and right sides were used to express the chromosomes. Optimal EEG electrode locations were found, and comparative analysis was performed on the differences based on membership function, number of clusters, and number of learning generations, learning algorithm, and wavelet settings. The performance evaluation results showed that the optimal EEG electrode locations were F7, F8, FC5, and FC6, whereas the accuracy of learning and test data of user-intention recognition was found to be 94.2% and 92.3%, respectively, which suggests that the proposed system can be used to recognize user intention for specific behavior. The system proposed in the present study can allow continued rehabilitation exercise in everyday living according to user intentions, which is expected to help improve the user's willingness to participate in rehabilitation and his or her quality of life.

Audio forensics and provenance analysis: Technologies for media verification and asset management

This paper discusses a number of audio forensics and provenance analysis technologies that were developed for media verification and media asset management, describing the motivation behind them, the methods used to develop them, and the way they are applied in practice. The paper also shows how the technologies may be adapted and applied to other domains. Finally, it outlines why it is crucial for research and development to follow an iterative, interactive process that does not require large budgets, but rather the willingness of users (here: broadcasters) and researchers to conduct experiments with limited risk and investment instead of waiting for ‘perfect projects’ and ‘perfect technologies’.

Consumers' Preferences and Willingness to Pay for Personalised Nutrition.

Personalised nutrition (PN) has great potential for disease prevention, particularly if coupled with the power and accessibility of mobile technology. However, success of PN interventions will depend on the willingness of users to subscribe. This study investigates the factors associated with potential users' perceived value of PN and heterogeneity in these values. A discrete choice experiment was carried out in a representative sample (N = 429 valid responses) from the adult population in Spain. The results were analysed in line with McFadden's Random Utility Theory, using conditional and mixed logit models in addition to a latent class logit model. The conditional and mixed logit models revealed the existence of a significant preference and willingness to pay for personalised nutrition, but the effect on average was not large for the highest level of personalisation. The latent class logit revealed four classes of respondent: those who would be likely to pay for a high level of personalised nutrition service, those who would use it if it were heavily subsidised, those who would use only a basic nutrition service, and those who would not be willing to engage. These results could be useful for the design and targeting of effective personalised nutrition services. Over half of adults currently perceive some individual benefit in a high level of PN, which may justify some degree of public subsidy in investment and delivery of such a service.

Influencing Factors on Consumers’ Willingness to Share Energy Data on Online Energy Platforms

Climate change requires an adaptation of the energy system towards an efficient use of renewable energies. For efficient control and optimization of the energy system, energy consumption and production data at household level play an essential role. Sharing platforms can enable the bundling and controlling of energy data from individual households. However, there is often a lack of acceptance among potential users to share their own data on such platforms. Therefore, this paper investigates the willingness of consumers to share their personal energy data. In particular, several factors that influence this willingness are examined. Decisive for the willingness are incentives for consumers in return for sharing their energy data. These can be offered in personal added value or collective added value. This paper shows that the factors perceived behavioral control, personal attitude and subjective norm have an influence on the willingness of private users to share energy data if a personal benefit or a collective benefit is provided. The age of users and their privacy concerns affect the willingness to share only in case personal value is added. These findings are valuable for the development and operation of online energy platforms.

A task recommendation framework for heterogeneous mobile crowdsensing

Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants' historical behaviors are analyzed, assuming that user behaviors can be quantified as the user's willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participant's preferred task list for sensing task recommendation. The experiment in this paper is carried out on the MATLAB platform based on two real check-in datasets, Gowalla and Brightkite. The results show that the average allocation precision rate can reach 96%, and the sensing user participation rate is about 97%. Meantime, the user's mobile cost is reduced, and the overall goal of maximizing accuracy and minimizing perceived cost is achieved.

Securing fingerprint templates by enhanced minutiae‐based encoding scheme in Fuzzy Commitment

Fingerprint authentication has gained attention in recent years due to its distinctiveness, low-cost sensors, and user willingness to submit them. Its extensive deployment in our automated world raises major concerns regarding the secrecy of biometric templates and the privacy of rightful owners in biometric systems. Fuzzy Commitment (FC) focuses on securing the biometric templates by performing authentication based on the validity of secret keys from biometric features. However, the major challenge in designing Fingerprint-based FC is the requirement of efficient binary representation for unordered and variable minutiae points in fingerprint images. Additionally, its leakage can compromise an intrinsic characteristic of the individual. The paper proposes Fingerprint-based FC incorporating an encoding scheme dependent on the number and type of minutiae points present near the fingerprint's core point. The biometric templates are never identical thereby, this approach allows fuzziness in minutiae points by incorporating Bose–Chaudhuri–Hocquenghem (BCH) codes for error-correction and secures random codeword of the error-correcting scheme as secret key by SHA-256 hash mapping. The proposed approach is evaluated on FVC2000-DB2, FVC2002-DB1, FVC2002-DB2, and FVC2004-DB1, and the results illustrate the efficiency of the proposed scheme. Furthermore, the security analysis of stored helper data and hash mapping demonstrates that the proposed approach is secure.

Users' Willingness to Share Health Information in a Social Question-and-Answer Community: Cross-sectional Survey in China.

BackgroundSocial question-and-answer communities play an increasingly important role in the dissemination of health information. It is important to identify influencing factors of user willingness to share health information to improve public health literacy.ObjectiveThis study explored influencing factors of social question-and-answer community users who share health information to provide reference for the construction of a high-quality health information sharing community.MethodsA cross-sectional study was conducted through snowball sampling of 185 participants who are Zhihu users in China. A structural equation analysis was used to verify the interaction and influence of the strength between variables in the model. Hierarchical regression was also used to test the mediating effect in the model.ResultsAltruism (β=.264, P<.001), intrinsic reward (β=.260, P=.03), self-efficacy (β=.468, P<.001), and community influence (β=.277, P=.003) had a positive effect on users’ willingness to share health information (WSHI). By contrast, extrinsic reward (β=−0.351, P<.001) had a negative effect. Self-efficacy also had a mediating effect (β=.147, 29.15%, 0.147/0.505) between community influence and WSHI.ConclusionsThe findings suggest that users’ WSHI is influenced by many factors including altruism, self-efficacy, community influence, and intrinsic reward. Improving the social atmosphere of the platform is an effective method of encouraging users to share health information.

Factors Predicting Patients’ Willingness to Use Robotic Dental Services

Prior research into automated dental procedures has largely focused on design and engineering issues; however, there is very little research that investigates the public’s perceptions of, or their willingness to undergo, robotic dentistry. Therefore, the purpose of the current study is to better understand the characteristics of a person who would be willing to undergo robotic dental cleanings by building a predictive model. Using a correlational research design with regression, two stages were utilized to complete the study. Through two stages of research, 475 participants were recruited via an online database. Stage one evaluated several statistical factors on user willingness to create a predictive regression model. Stage two employed a secondary sample to validate the predictive model created in stage one by testing the regression equation for model fit. This predictive model accounted for approximately 82% of the variance in willingness to use dental robots for cleanings. The significant predictors were feelings of fear, feelings of happiness, complexity, useful factor, fun factor, fear of dental visits, and age respectively. These results are useful for the design and marketing of dental robots.

Exploring Preferences for Transportation Modes in the City of Munich after the Recent Incorporation of Ride-Hailing Companies

The growth of ride-hailing (RH) companies over the past few years has affected urban mobility in numerous ways. Despite widespread claims about the benefits of such services, limited research has been conducted on the topic. This paper assesses the willingness of Munich transportation users to pay for RH services. Realizing the difficulty of obtaining data directly from RH companies, a stated preference survey was designed. The dataset includes responses from 500 commuters. Sociodemographic attributes, current travel behavior and transportation mode preference in an 8 km trip scenario using RH service and its similar modes (auto and transit), were collected. A multinomial logit model was used to estimate the time and cost coefficients for using RH services across income groups, which was then used to estimate the value of time (VOT) for RH. The model results indicate RH services’ popularity among those aged 18–39, larger households and households with fewer autos. Higher income groups are also willing to pay more for using RH services. To examine the impact of RH services on modal split in the city of Munich, we incorporated RH as a new mode into an existing nested logit mode choice model using an incremental logit. Travel time, travel cost and VOT were used as measures for the choice commuters make when choosing between RH and its closest mode, metro. A total of 20 scenarios were evaluated at four different congestion levels and four price levels to reflect the demand in response to acceptable costs and time tradeoffs.

A user collaboration privacy protection scheme with threshold scheme and smart contract

Recently, the revolution of 5G networks has promoted a wide application of wireless technology, and then followed with the prosperity of Location-Based Service (LBS). However, an intricate problem called privacy violation still restricts the development of this type of service. To preserve users’ privacy, some privacy preservation strategies had been proposed. As an efficient framework used for protecting personal privacy, users’ collaboration is mainly proposed for dealing with the issue of service bottleneck and the issue of attack focus. However, there are still several challenges far from being perfectly solved, such as the collusion attacks as well as the willingness of collaborative users. Therefore, to address these challenges, in this paper, based on the conception of information division and user’s incentive, a (t,n) smart contract privacy preservation scheme short for (t,n) SCPPS is proposed. In this scheme, the query information is encrypted and split by (t, n) threshold scheme, so the collaborative users will be difficult to learn anything about the initiator, and the collusion will be difficult to constitute the integral query without t users colluding with each other. On the other hand, the combination of (t, n) threshold scheme and smart contract further provide incentive competition, as just the earliest collaborative users that submit the partition information and get the corresponding feedback will obtain the incentive. At last, the security analyses were given, and then tests of users’ granularity and locations difference in the continuous query in the real environment. Furthermore, simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme, and the experimental verification is executed using two different types of datasets. Thus, compared with other similar schemes, the results with brief explanations about reasons can further demonstrate the superiority of the proposed scheme.

Designing a smart integrated workspace to improve building energy efficiency: an Indonesian case study

The increased energy consumption in office building led to stakeholders’ renewed interests in optimizing office buildings’ energy performances, such as indoor comfort, energy efficiency, and life cycle costs. One of the most effective solutions to hedging against building operation optimization is incorporating energy efficiency measures and architectural programming early. This paper proposes a Smart Integrated Workspace Design (SIWD) framework that integrates architectural programming system using Internet of Things (IoT) technology into the Value Engineering (VE) process to assess design alternatives targeted explicitly at future smart office buildings. The use of the SIWD led to an improved development cycle linking architectural plans and engineering design. An empirical test using a large building owner, their designers, and contractors in Jakarta, Indonesia, resulted in an 18% improvement in building energy performance and quality when the design team used the SIWD framework. It is also indicated that leveraging advanced technology and incorporating familiar concepts and processes increased user willingness to adopt the SIWD in future projects. So, incorporated the IoT in architectural design and evaluated by VE made the readiness and friendly technology to all occupants to get the building efficiency and comfort unsteady.

User-Generated Content Regulation for Mixed Two-Sided Platforms

Mixed two-sided platforms suffer from the dilemma related to the platform’s regulation of user-generated content (UGC). Strict regulation may weaken the willingness of users to continuously generate new content, whereas loose regulation may lead to massive low-quality content shares. This study develops a game-theoretic model to address the optimal level of platform regulation and the factors that influence the platform’s revenue model decisions. Two revenue models are generally adopted by a UGC platform: access price and advertisement agency. We find that the positive effect of platform regulation and the efficiency of ads has significant influence on the results related to optimal regulation level and revenue model. In particular, when the positive effect of platform regulation is minimal (maximal), regular (maximal) platform regulation is optimal. Moreover, when the efficiency of ads is positive, the platform under the advertisement agency model generates more revenue; otherwise, the platform under the access price model does. When the positive effect of platform regulation is moderate, the results for optimal regulation level and revenue model are jointly determined by the mixed influence of the efficiency of ads and the positive effect of regulation. We also find that a win-win-win scenario for platform profit, consumer surplus, and social welfare may occur. This study contributes to our understanding of the economic analysis of platform regulation. Our findings also provide insights into the strategy of platform regulation and the impact of platform regulation on a platform’s revenue model decisions.

A Study of the Factors That Influence the Willingness of Users to Leave Comments on E-Commerce Platforms

This study aims to better understand the elements of e-commerce platforms that cause users to be more willing to leave behind comments. The authors summarize the literature of influencing factors and construct an influencing factor model for user comments. Self-expression questionnaires were used to provide data for structural equation modeling (SEM). They found that user reputation, perceived moral responsibility, and emotional tendency each exert a positive impact on the willingness to comment; that reciprocity psychology moderates the correlation between moral responsibility and commenting willingness; and that platform feedback moderates the correlation between economic reward and emotional tendency. This study helps merchants better understand when users are more likely to leave comments helpful to improving customer satisfaction levels.

Basic life support training for the adult lay population. A systematic review

Cardiopulmonary resuscitation (CPR) training is an influential method for improving the perception of resuscitation maneuvers, user willingness to carry it out, and also coordination of the people performing it. The main objective of the present systematic review is to evaluate the effects of basic CPR training program and Semi-Automatic External Defibrillation (AED) use on acquiring and retaining practical skills of lay adults. The included researches involved adult participants of 18 years old and over without previous CPR official training. In addition, the studies containing any training program, without a specified training method of CPR and AED use, were considered. A qualitative analysis was conducted to determine the most efficient B-CPR and AED use training program for lay adults regarding to acquisition and retention of practical skills. All randomized experimental trials followed a sequence of assigning participants to different categories. This corresponds to distinct interventions which in turn involved various CPR and AED training types or different durations of training between the groups. Overall, a specific training methodology has not been identified for approaching a clear demonstration with more effective and low-cost results. However, it seems that a training program based on simplified and short-term CPR combined with instructor-student feedback, videos, feedback devices, and simulations could cause reduction of the barriers for the lay population to help the OHCA patients and perform CPR.