Large Group Of Users Research Articles

FAKTOR MEMPENGARUHI PERILAKU KECANDUAN SOSIAL MEDIA

Social media is a digital platform product that has received a positive response from people. Social media is no longer part of tertiary or primary needs but secondary needs. For users, social media has become part of their daily lives, and teenagers to young adults are the largest group of users, causing addictive behavior towards their users. Addiction to social media can be influenced by several factors such as psychosocial. Many researchers use the cross-sectional method in their research. This systematic review aims to analyze 12 international journals regarding the factors that influence social media addiction, and psychosocial (depression, anxiety, FoMO, and so on) are reported to have the most influence on social media addiction. The other variables are bullying, cyberbullying, self-esteem, and cultural influence. Types of social media addiction used in educational research are associated with decreased academic achievement due to poor sleep quality. The aspects of social media addiction are emotional, behavioral, affective, and cognitive. These findings provide recommendations for conducting more experimental research related to social media addiction in students.

Data management system for diabetes clinical trials: a pre-post evaluation study

BackgroundData management system for diabetes clinical trials is used to support clinical data management processes. The purpose of this study was to evaluate the quality and usability of this system from the users’ perspectives.MethodsThis study was conducted in 2020, and the pre-post evaluation method was used to examine the quality and usability of the designed system. Initially, a questionnaire was designed and distributed among the researchers who were involved in the diabetes clinical trials (n = 30) to investigate their expectations. Then, the researchers were asked to use the system and explain their perspectives about it by completing two questionnaires.ResultsThere was no statistically significant differences between the users’ perspectives about the information quality, service quality, achievements, and communication before and after using the system. However, in terms of the system quality (P = 0.042) and users’ autonomy (P = 0.026), the users’ expectations were greater than the system performance. The system usability was at a good level based on the users’ opinions.ConclusionIt seems that the designed system largely met the users’ expectations in most areas. However, the system quality and users’ autonomy need further attentions. In addition, the system should be used in multicenter trials and re-evaluated by a larger group of users.

Betel Nuts, Health Policies, and Adolescent Health

ABSTRACT Areca nut and betel quid (AN/BQ) products are largely scrutinized by the scientific community because of their toxicological and carcinogenic properties. However, at the same time there exists an ever-growing user base in low- and medium-income countries, whose users are responding to innovative products preparation processes and are initiated to AN/BQ products by their parents and family at a young age. This report compiles current cessation policies, implemented interventions, and comments on their corresponding effectiveness and/or potential effectiveness. The report also highlights the need for further research from both an adolescent health and a Chinese perspective, as data regarding the region with the second largest user group after India are largely unknown or unavailable for scientific review. Ultimately, recent studies involving analytical methods to observe how different cultivation environments, and/or processing methods change the chemical composition of the AN/BQ product have also presented a potential insight in better understanding and eventually regulating AN/BQ across all population groups, including adolescents. The rise of digital solutions may also encourage development of applications to track consumption and usage and distribution of AN/BQ products for policy makers to design targeted campaigns.

“TEG” talks: technology worth spreading?

“TEG” talks: technology worth spreading?

Psychometric properties of the client-centred rehabilitation questionnaire-is derived from a large and varied group of rehabilitation users

Purpose The purpose of this study was to inspect and establish the factor structure of the Icelandic Client-Centred Rehabilitation Questionnaire [CCRQ-is] and investigate the service experiences of a large and varied sample of rehabilitation users. Materials and methods Altogether 499 rehabilitation users completed the questionnaire. Confirmatory and exploratory factor analysis was conducted for analysis of items. Mann-Whitney’s U and Kruskal-Wallis test was used to compare subscale responses based on participants’ characteristics. Results Four factors explained 53,2% of the variance: Respect and attentiveness, Interaction with significant others, Responsiveness to needs and preferences and Education and enablement. Subscales showed internal consistency from 0.72–0.91 and 0.92 for the whole instrument. The subscale Respect and attentiveness represented user centred rehabilitation the most and Interaction with significant others the least. Significant differences in relation to health conditions and age were obtained on all four subscales and differences by gender on one subscale. Conclusions Our results suggest the CCRQ-is is a reliable tool that can be used with rehabilitation users with a broad range of characteristics within the Icelandic context. The extent to which the intersection of age, gender and health issues influence users’ perception of services needs to be further scrutinized.

Dementia Research on Facebook and Twitter: Current Practice and Challenges.

Social media is a powerful tool for engaging diverse audiences in dementia research. However, there is little data summarizing current content exchange in this context. To inform ethical dementia research engagement on social media, we characterized current practices by analyzing public social media posts. We retrieved Facebook (2-year period, N = 7,896) and Twitter (1-year period, N = 9,323) posts containing dementia research-related keywords using manual and machine learning-based search strategies. We performed qualitative and quantitative content and sentiment analyses on random samples (10%) of the posts. Top Facebook users were advocacy (45%) and health organizations (25%). On Twitter, academics/researchers were the largest user group. Prevention was the most frequently coded theme (Facebook 30%; Twitter 26%), followed by treatment (Facebook 15%; Twitter 18%). Diagnostics had the highest Facebook engagement. Sharing knowledge was the primary form of content exchange (Facebook 63%; Twitter 80%). Most shared journal articles were peer-reviewed and open access. Emotional tone was overall more positive on Facebook. Justice was a prominent ethics topic regarding inequalities related to identity and intersecting modes of marginalization in dementia research. The findings indicate the importance of social media as an engagement tool of current topics in health research and reveal areas of potential for increased engagement. These data can inform consensus-based best practices for ethical social media application in dementia research.

Investigating the Provision and Context of Use of Hearing Aid Listening Programs From Real-world Data: Observational Study.

Listening programs enable hearing aid (HA) users to change device settings for specific listening situations and thereby personalize their listening experience. However, investigations into real-world use of such listening programs to support clinical decisions and evaluate the success of HA treatment are lacking. We aimed to investigate the provision of listening programs among a large group of in-market HA users and the context in which the programs are typically used. First, we analyzed how many and which programs were provided to 32,336 in-market HA users. Second, we explored 332,271 program selections from 1312 selected users to investigate the sound environments in which specific programs were used and whether such environments reflect the listening intent conveyed by the name of the used program. Our analysis was based on real-world longitudinal data logged by smartphone-connected HAs. In our sample, 57.71% (18,663/32,336) of the HA users had programs for specific listening situations, which is a higher proportion than previously reported, most likely because of the inclusion criteria. On the basis of association rule mining, we identified a primary additional listening program, Speech in Noise, which is frequent among users and often provided when other additional programs are also provided. We also identified 2 secondary additional programs (Comfort and Music), which are frequent among users who get ≥3 programs and usually provided in combination with Speech in Noise. In addition, 2 programs (TV and Remote Mic) were related to the use of external accessories and not found to be associated with other programs. On average, users selected Speech in Noise, Comfort, and Music in louder, noisier, and less-modulated (all P<.01) environments compared with the environment in which they selected the default program, General. The difference from the sound environment in which they selected General was significantly larger in the minutes following program selection than in the minutes preceding it. This study provides a deeper insight into the provision of listening programs on a large scale and demonstrates that additional listening programs are used as intended and according to the sound environment conveyed by the program name.

Aggregation in bottom-up vulnerability assessments and equity implications: The case of Jordanian households’ water supply

Bottom-up methods for water resources modeling rely on acceptability thresholds to find, through a response surface, which deeply uncertain futures lead to system failure. They commonly treat water users as aggregate actors, which may preclude analysis of the equity impacts of interventions. This paper explores how aggregation choices for large groups of water users lead to different policy recommendations in response surface assessments. Two aggregation methods with varying parameters are considered: percentile satisfaction targets and generalized mean. A 2-dimensional stress-test assessment across groundwater availability and population is applied to household water supply in Jordan. The study compares six different policies covering supply enhancement and rebalancing, using a country-wide multi-agent model that characterizes households across socioeconomic strata. For different aggregation levels, policies are ordered by their associated robustness index. Results show that aggregation choices strongly determine policy preference. A focus on the most vulnerable households favors the equalization of access to water, in terms of regional allocation and weekly supply durations, as it substantially reduces robustness disparity. Combined policies with additional resources allow to withstand higher levels of stress under most aggregation choices. Preferences defined by aggregation intervals provide a finer understanding of trade-offs among water users and may improve deliberation over equity under deep uncertainty.

A computational study on the basis for a safe speed limit for bicycles on shared paths considering the severity of pedestrian head injuries in bicyclist-pedestrian collisions

Bicyclists and pedestrians are two large vulnerable groups of road users. Many cities have allowed cyclists to share space with pedestrians on footpaths and off-road paths to reduce conflict with motor vehicles. The risk of bicyclist-pedestrian accidents is also increasing accordingly. Therefore, there is a need to understand the factors that affect the risk of injury in such accidents, especially to pedestrians who are considered more vulnerable. This paper presents a detailed investigation of bicyclist-pedestrian collisions and possible injury outcomes. The study has considered five levels of collision speed ranging from 10 km/h to 30 km/h, three pedestrian profiles (adult, child, and elderly) differentiated by their weight and height, three bicycles with different masses, and five impact directions. The bicyclist-pedestrian collision simulations have been analyzed based on four metrics: throw distance, peak head velocity on impact with the ground, head injury criterion (HIC) value, and the probability of severe head injury. For each simulation, the throw distance and peak head velocity on impact with the ground are extracted. Following that, the HIC and the probability of severe head injury to pedestrians are computed. The results show a significant effect of collision speed (p < 0.05) on all four metrics. The analysis has been further extended to study the effect of height and weight profile, bicycle mass, and impact directions on bicyclist-pedestrian collisions. According to the results, the impact directions largely influence the outcome of bicycle-pedestrian collisions. In general, direct impacts on pedestrian body center have been found to yield higher HIC values and probability of severe head injury to pedestrians than off-center impacts. Also, video analysis of simulated collisions has suggested that the accident mechanism depends on weight and height profiles (correlated with different age groups) and impact directions. Finally, recommendations have been proposed based on the study, including a speed limit of not more than 12 km/h for bicyclists on narrow shared paths and footpaths where risks of collisions with pedestrians are high. The results and analysis presented could be helpful for developing legislation to minimize conflicts between bicyclists and pedestrians on shared paths and to reduce potential injury to pedestrians.

Home-Based Audiometry With a Smartphone App: Reliable Results?

The aim of this study was to assess the test-retest reliability of a smartphone-based hearing test, performed without supervision of a hearing professional in an uncontrolled environment. The hearing application is based on an automated hearing test (DuoTone) and relies on verification procedures (ambient noise monitoring algorithm, graphical user interface) to ensure appropriate measurement conditions. Thresholds obtained with DuoTone were compared to those obtained with standard clinical audiometry for 0.5, 1, 2, and 4 kHz in 13 subjects. Subsequently, test-retest reliability was analyzed using anonymized cloud-stored data from a large group of app users (1,641 subjects) who performed multiple hearing tests. Thresholds at minimum or maximum presentation level of the hearing test (10 dB HL, 85 dB HL) were excluded to avoid floor/ceiling effects. A subset (500 subjects) was created to exclude potentially unreliable data. Test-retest thresholds were compared at 12 test frequencies, from 125 Hz to 12 kHz. Thresholds determined by DuoTone and clinical audiometry did not differ significantly for each test frequency. Regarding test-retest analysis, the percentage of test-retest results within 5 dB ranged from 60% to 77% per test frequency. Results from the subset were not substantially different. Test-retest reliability for app users was comparable to results published in the literature regarding test-retest reliability of audiometry, performed in the clinic. Initial validation results suggest that thresholds obtained with DuoTone are comparable to clinical audiometry (four frequencies tested). The hearing app provides reliable hearing thresholds between 15 and 80 dB HL (12 frequencies tested) with a test-retest reliability comparable to clinical audiometry.

Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction

Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study, and analyzes the problems of "big data volume", "cold start" and "data sparsity" in the recommender system in modern business websites. In response to these problems, this paper transforms the problem of large data volume into the problem of large user groups. Then, after using the k-means clustering algorithm to divide the large user group into homogeneous user groups to alleviate the problem, a combination of collaborative filtering algorithm and content-based recommendation algorithm in the homogeneous user group is proposed to alleviate this problem. The experimental precision and recall are both around 0.4, and when W=0.8, the F value is the largest.

Making Space a better Place: Just In Time Adaptive Interventions for Healthy Lifestyles

Abstract. Just In Time Adaptive Interventions (JITAI) are a class of applications that provide tailored support at the right time and place to help people to maintain a healthy lifestyle. This paper presents the initial version of a JITAI application that helps users to make healthy food choices when walking in an urban environment. The app provides interventions based on location, time, and user characteristics. It adapts its interventions based on the type and goal of a user. The JITAI consist of an app for a mobile phone and a backend server. Using the backend, decision points and interventions can be managed as well as users. Moreover, it stores tracks and intervention data. A two-phased experiment will be set-up. The first phase will test the robustness and functionality of the application with a limited number of users. Based on this, the app will be adapted. The second phase will test the effectiveness of the different type of interventions by involving a large user group.

HARNESSING HUMAN-CENTRED DESIGN APPROACHES TO BUILD A CHATBOT FOR PEOPLE LIVING WITH HYPERTENSION

Objective: To describe how human-centered design was used to create an artificial intelligence (AI) enabled chatbot for people living with hypertension. Design and method: The project focused on technology augmentation and testing to strengthen the mDoc’s CompleteHealthTM platform (an omnichannel platform that provides self-management techniques digitally to people with chronic diseases) to enable us to drive scale in providing self-care support. We leveraged Human-Centered Design principles to guide our pathway based on inspiration, ideation, and implementation to design a chatbot called “Kem’’, aimed to provide real-time response to members living with hypertension. In the inspiration phase, our clinical and tech teams hypothesized that 80% of our growing members’ questions on hypertension could be answered by Kem at scale. In the ideation phase, we leveraged our members to understand and develop the conversation structure and conducted extensive research on how to provide real-time responses to as many members as possible. Kem’s prototype was developed on Botmock (a tool used for designing and fleshing out conversational design flow) with a persona designed to function as a health coach to help bridge the gap in knowledge of health topics, guide, and answer mDoc members. After a series of iterations based on users’ feedback, during the implementation phase, Kem was integrated into a chat platform, Telegram, and was tested with a larger group of users. Results: By applying human-centered design and integrating user feedback into the design and creation of a chatbot to answer questions around hypertension, we were able to create a tool that will help members increase their knowledge of the prevention and management of hypertension. Conclusions: Co-creation should occur at every step of the journey when designing for members’ needs and behaviors. The users’ expressed excitement about Kem and the design process revealed that we needed to be more intentional about making the conversation with the bot interesting, user-friendly, and less technical. As the implementation phase continues and more members use Kem, we will be able to educate them about the prevention and management of hypertension.

Differentially private recommender system with variational autoencoders

To provide precise recommendations, traditional recommender systems (RS) collect personal data, user preference and feedback, which are sensitive to each user if such information is maliciously used for extra analysis. In recent years, differential privacy (DP) has been widely applied in RS to provide privacy protection for sensitive information. Prior studies explored the combination of DP and RS, while neglecting the disparate effect on model accuracy of imbalanced subgroups as large user groups control the trained model, and DP can worsen the disparate effect of degrading the performance of recommender systems significantly. Besides, the number of uploaded contributions can differ among users for training a recommender system, so it is necessary to set the user-level privacy guarantee.In this paper, we make four contributions. First, we propose an efficient way of constructing datasets for training a recommender system based on prior theories. Second, we compute the user-level priors based on user metadata to optimize the VAE model. Besides, we add noise into the calculation process to protect user metadata. Third, we analyze and propose a tighter theoretical bound on gradient updates for DP Stochastic Gradient Descent (DPSGD). Finally, we exploit these theoretical results and propose a novel DP-VAE based recommender system. Extensive experimental results on multiple datasets show that our system can achieve high recommendation precision while maintaining a reasonable privacy guarantee.

Effects of Naturalistic Psychedelic Use on Depression, Anxiety, and Well-Being: Associations With Patterns of Use, Reported Harms, and Transformative Mental States

Survey-based studies suggest naturalistic psychedelic use provides mental health benefits similar to those observed in clinical trials. The current study sought to confirm these findings in a large group of psychedelic users and to conduct a novel examination of associations between amount of psychedelic use and behavioral outcomes, as well as frequency of harms ascribed to psychedelic use. A cross-sectional, online survey was completed by 2,510 adults reporting at least one lifetime psychedelic experience. Participants retrospectively completed a battery of instruments assessing depression, anxiety, and emotional well-being prior to and following psychedelic exposure. Participants also reported preferred psychedelic agent, number of uses, and harms attributed to psychedelic use. Psychedelic use was associated with significant improvements in depressive and anxious symptoms and with increased emotional well-being. These improvements increased in magnitude with increasing psychedelic exposure, with a ceiling effect. However, improvements were noted following a single lifetime use. Strong evidence for benefit of one preferred psychedelic agent over another was not observed, but enduring increases in factors related to mystical-experience and prosocial perspective taking associated with enhanced mental health. Thirteen percent of the survey sample (n = 330) endorsed at least one harm from psychedelic use, and these participants reported less mental health benefit. Results from the current study add to a growing database indicating that psychedelic use—even outside the context of clinical trials—may provide a wide range of mental health benefits, while also posing some risk for harm in a minority of individuals.

Scoping Review of Bioelectrical Signals Uses in Videogames for Evaluation Purposes

Bioelectric signals can improve assessment in videogames by helping to better understand user experience, evaluate attention, or study the cognitive and physical effects of games. Use of signals is therefore relevant to better evaluate and understand the impact and effects of videogames on players, and specially so in the field of serious games, such as educational or training games, to gain insights into the learning processes; or in games for health to better evaluate non-conscious effects on the player’s body. We examine how biological signals (bioelectric + eye-tracking) are being used and collected in the field of video games, including the choice of signals, the devices used to collect them (e.g., wearables), the purposes for which they are collected, and the results reported from their use. As a result of this systematic literature review, 81 articles have been analyzed, finding that electrocardiograms and encephalograms are the most frequently used signals. The main use of these bioelectrical signals is to evaluate player engagement, level of difficulty, and stress during the gameplay. But there are also examples where signals are used to detect health problems, or as evidence to compare educational games with other learning activities. This review informs researchers interested in better understanding the benefits and limitations of biological signals for video games, providing an overview of studies conducted in recent years and the associated devices described in those studies. Limitations in this field include signal noise issues as well as the amount of time required to calibrate the devices during experiments, adding to the complexity of user testing. It is necessary to work on tools that facilitate experiments with large groups of users in parallel as well as to work on open software and low-cost devices that allow the emergence of a greater number of studies in this field, given for example their potential in the field of educational games to better understand the learning processes of users.

Avoiding the Hook: Influential Factors of Phishing Awareness Training on Click-Rates and a Data-Driven Approach to Predict Email Difficulty Perception

Phishing attacks are still seen as a significant threat to cyber security, and large parts of the industry rely on anti-phishing simulations to minimize the risk imposed by such attacks. This study conducted a large-scale anti-phishing training with more than 31000 participants and 144 different simulated phishing attacks to develop a data-driven model to classify how users would perceive a phishing simulation. Furthermore, we analyze the results of our large-scale anti-phishing training and give novel insights into users’ click behavior. Analyzing our anti-phishing training data, we find out that 66% of users do not fall victim to credential-based phishing attacks even after being exposed to twelve weeks of phishing simulations. To further enhance the phishing awareness-training effectiveness, we developed a novel manifold learning-powered machine learning model that can predict how many people would fall for a phishing simulation using the several structural and state-of-the-art NLP features extracted from the emails. In this way, we present a systematic approach for the training implementers to estimate the average "convincing power" of the emails prior to rolling out. Moreover, we revealed the top-most vital factors in the classification. In addition, our model presents significant benefits over traditional rule-based approaches in classifying the difficulty of phishing simulations. Our results clearly show that anti-phishing training should focus on the training of individual users rather than on large user groups. Additionally, we present a promising generic machine learning model for predicting phishing susceptibility.

A Lightweight Auditing Service for Shared Data with Secure User Revocation in Cloud Storage

As data sharing has become one of the most popular services offered by cloud storage, designing public auditing mechanisms for integrity of shared data becomes more important. Two problems which arise in shared data auditing include preserving users identity and collusion resistant revocation of users. When data stored at the cloud is shared among a group of users, different users may modify and sign different data blocks which leaks signer identities to the public verifier. Also, when a user is revoked from the group, signatures generated by this user should be re-signed by the cloud server using re-signature keys. In addition, collusion of cloud server and the revoked user should leak no information about the private key of other users. In this paper, by employing a new proxy re-signature scheme, we propose a public shared data auditing mechanism that provides identity privacy and collusion resistant user revocation, simultaneously. The proposed protocol requires only lightweight computations at the user side for signing data blocks in real-time online phase. Moreover, our protocol supports large dynamic group of users, batch auditing and dynamic data operations. Experimental results demonstrate excellent efficiency of our scheme in comparison to the state of the art.

Campus Design of Universities: An Overview

This paper reviews literature to understand the field of Campus Design and the factors impacting campus design in universities. Literature Review shows that most literature in the field of Campus Design has been authored by practising professionals and the attempt at academic research is relatively new in the field. This leads to a few often-quoted literature sources both in the global and the Indian scenario. Research into the aspect of physical planning of a campus and the quality of space as attributes to achieving effective teaching-learning and university objectives is only recently being considered. Most literature available is case-study based. Campus design parameters that help achieve university objectives need to be identified at the master plan level. Most researches, study campuses with a microscopic view of learning spaces, space comfort etc. Gross design issues which impact teaching-learning-research in Universities and provide a wholesome experience to largest user group, the student community, needs research. The new challenges faced by Indian university campuses, like the incumbent New Education Policy 2020 and situations like a global pandemic, which may affect campus design have been identified as parameters to study and consider when proposing campus design guidelines.

Power structure builds gamer loyalty

Online games offer substantial business opportunities, fueling intense competition among game makers. To build gamer loyalty, a critical challenge for game makers is preventing gamers' misbehaviors that annoy other gamers. Such challenges should be considered when game makers are making their design decisions and resource allocation decisions. We contend that building in-game relational cohesion should be an effective means of reducing such behaviors and strengthening loyalty. We also extend relational cohesion theory (RCT) by challenging the assumption that only equal power helps form relational cohesion in one-on-one contexts. In contrast, unequal power may provide an alternative means of enhancing relational cohesion in large groups of users (e.g., the entire user population). This study is the first to introduce and explore how unequal power—a novel and critical element of power structure—forms relational cohesion and strengthens online gamer loyalty. This study uses both behavioral-continuous and perceptual-anchored scales to collect responses from 1003 online gamers. Theoretically, this study widens the applicability of RCT from one-on-one contexts to one-to-many contexts. Practically, this study can help game makers make design and resource allocation decisions to use unequal power, thus effectively building gamer loyalty.