A Web Application About Herd Immunity Using Personalized Avatars: Development Study.

BACKGROUND: Herd immunity or community immunity refers to the reduced risk of infection among susceptible individuals in a population through the presence and proximity of immune individuals. Recent studies suggest that improving the understanding of community immunity may increase intentions to get vaccinated. OBJECTIVE: This study aims to design a web application about community immunity and optimize it based on users' cognitive and emotional responses. METHODS: Our multidisciplinary team developed a web application about community immunity to communicate epidemiological evidence in a personalized way. In our application, people build their own community by creating an avatar representing themselves and 8 other avatars representing people around them, for example, their family or coworkers. The application integrates these avatars in a 2-min visualization showing how different parameters (eg, vaccine coverage, and contact within communities) influence community immunity. We predefined communication goals, created prototype visualizations, and tested four iterative versions of our visualization in a university-based human-computer interaction laboratory and community-based settings (a cafeteria, two shopping malls, and a public library). Data included psychophysiological measures (eye tracking, galvanic skin response, facial emotion recognition, and electroencephalogram) to assess participants' cognitive and affective responses to the visualization and verbal feedback to assess their interpretations of the visualization's content and messaging. RESULTS: Among 110 participants across all four cycles, 68 (61.8%) were women and 38 (34.5%) were men (4/110, 3.6%; not reported), with a mean age of 38 (SD 17) years. More than half (65/110, 59.0%) of participants reported having a university-level education. Iterative changes across the cycles included adding the ability for users to create their own avatars, specific signals about who was represented by the different avatars, using color and movement to indicate protection or lack of protection from infectious disease, and changes to terminology to ensure clarity for people with varying educational backgrounds. Overall, we observed 3 generalizable findings. First, visualization does indeed appear to be a promising medium for conveying what community immunity is and how it works. Second, by involving multiple users in an iterative design process, it is possible to create a short and simple visualization that clearly conveys a complex topic. Finally, evaluating users' emotional responses during the design process, in addition to their cognitive responses, offers insights that help inform the final design of an intervention. CONCLUSIONS: Visualization with personalized avatars may help people understand their individual roles in population health. Our app showed promise as a method of communicating the relationship between individual behavior and community health. The next steps will include assessing the effects of the application on risk perception, knowledge, and vaccination intentions in a randomized controlled trial. This study offers a potential road map for designing health communication materials for complex topics such as community immunity.

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection.

Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.

African foods for deep learning-based food recognition systems dataset.

African foods have socio-cultural significance that extends through migration, tourism, and marriage. Africans travel and integrate within the continent through intermarriages. There are over a thousand cultural aspects that differ such as language, food, dressing, beliefs and customs. Food is one of the cultural aspects that Africans embrace quickly as they migrate and integrate socio-culturally. We considered the limited representation of African food research in the HCI community and propose to contribute the rich significant food datasets from two African countries: Cameroon and Ghana. List of Cameroonian foods collected are: Ekwang, Eru and Ndole. In addition, the list of Ghanaian foods we collected are: Jollof Rice, Palm-nut Soup and Waakye. Given the cultural diversity and our study's goal for cultural inclusion, we interacted with at least two locals from the selected countries, and they confirmed that these foods were universally recognized within their respective countries. The datasets were collected from YouTube, Facebook, the field (restaurants), Creative Common Attribution Google Images, and other Creative Commons Attribution sources. A total of 204 images of Ekwang, 206 images of Eru and 205 images of Ndole were collected. In addition, we collected a total of 347 images of Jollof Rice, 392 images of Palm-nut Soup and 400 images of Waakye. We present a meta-data description of the data, quality assessments of our dataset and opportunities for the HCI community to explore in the future.

Identifying adverse drug reactions from patient reviews on social media using natural language processing.

Drugs have the potential of causing adverse reactions or side effects and prior knowledge of these reactions can help prevent hospitalizations and premature deaths. Public databases of common adverse drug reactions (ADRs) depend on individual reports from drug manufacturers and health professionals. However, this passive approach to ADR surveillance has been shown to suffer from severe under-reporting. Social media, such as online health forums where patients across the globe willingly share their drug intake experience, is a viable and rich source for detecting unreported ADRs. In this paper, we design an ADR Detection Framework (ADF) using Natural Language Processing techniques to identify ADRs in drug reviews mined from social media. We demonstrate the applicability of ADF in the domain of Diabetes by identifying ADRs associated with diabetes drugs using data extracted from three online patient-based health forums: askapatient.com, webmd.com, and iodine.com. Next, we analyze and visualize the ADRs identified and present valuable insights including prevalent and less prevalent ADRs, age and gender differences in ADRs detected, as well as the previously unknown ADRs detected by our framework. Our work could promote active (real-time) ADR surveillance and also advance pharmacovigilance research.

A panoramic view of personalization based on individual differences in persuasive and behavior change interventions.

Persuasive technologies are designed to change human behavior or attitude using various persuasive strategies. Recent years have witnessed increasing evidence of the need to personalize and adapt persuasive interventions to various users and contextual factors because a persuasive strategy that works for one individual may rather demotivate others. As a result, several research studies have been conducted to investigate how to effectively personalize persuasive technologies. As research in this direction is gaining increasing attention, it becomes essential to conduct a systematic review to provide an overview of the current trends, challenges, approaches used for developing personalized persuasive technologies, and opportunities for future research in the area. To fill this need, we investigate approaches to personalize persuasive interventions by understanding user-related factors considered when personalizing persuasive technologies. Particularly, we conducted a systematic review of 72 research published in the last ten years in personalized and adaptive persuasive systems. The reviewed papers were evaluated based on different aspects, including metadata (e.g., year of publication and venue), technology, personalization dimension, personalization approaches, target outcome, individual differences, theories and scales, and evaluation approaches. Our results show (1) increased attention toward personalizing persuasive interventions, (2) personality trait is the most popular dimension of individual differences considered by existing research when tailoring their persuasive and behavior change systems, (3) students are among the most commonly targeted audience, and (4) education, health, and physical activity are the most considered domains in the surveyed papers. Based on our results, the paper provides insights and prospective future research directions.

Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach.

Multiple waves of COVID-19 have significantly impacted the emotional well-being of all, but many were subject to additional risks associated with forced regulations. The objective of this research was to assess the immediate emotional impact, expressed by Canadian Twitter users, and to estimate the linear relationship, with the vicissitudes of COVID caseloads, using ARIMA time-series regression. We developed two Artificial Intelligence-based algorithms to extract tweets using 18 semantic terms related to social confinement and locked down and then geocoded them to tag Canadian provinces. Tweets (n = 64,732) were classified as positive, negative, and neutral sentiments using a word-based Emotion Lexicon. Our results indicated: that Tweeters were expressing a higher daily percentage of negative sentiments representing, negative anticipation (30.1%), fear (28.1%), and anger (25.3%), than positive sentiments comprising positive anticipation (43.7%), trust (41.4%), and joy (14.9%), and neutral sentiments with mostly no emotions, when hash-tagged social confinement and locked down. In most provinces, negative sentiments took on average two to three days after caseloads increase to emerge, whereas positive sentiments took a slightly longer period of six to seven days to submerge. As daily caseloads increase, negative sentiment percentage increases in Manitoba (by 68% for 100 caseloads increase) and Atlantic Canada (by 89% with 100 caseloads increase) in wave 1(with 30% variations explained), while other provinces showed resilience. The opposite was noted in the positive sentiments. The daily percentage of emotional expression variations explained by daily caseloads in wave one were 30% for negative, 42% for neutral, and 2.1% for positive indicating that the emotional impact is multifactorial. These provincial-level impact differences with varying latency periods should be considered when planning geographically targeted, time-sensitive, confinement-related psychological health promotion efforts. Artificial Intelligence-based Geo-coded sentiment analysis of Twitter data opens possibilities for targeted rapid emotion sentiment detection opportunities.

Improving Mental Health Literacy and Reducing Psychological Problems Among Teachers in Zambia: Protocol for Implementation and Evaluation of a Wellness4Teachers Email Messaging Program.

BACKGROUND: Primary, basic, secondary, and high school teachers are constantly faced with increased work stressors that can result in psychological health challenges such as burnout, anxiety, and depression, and in some cases, physical health problems. It is presently unknown what the mental health literacy levels are or the prevalence and correlates of psychological issues among teachers in Zambia. It is also unknown if an email mental messaging program (Wellness4Teachers) would effectively reduce burnout and associated psychological problems and improve mental health literacy among teachers. OBJECTIVE: The primary objectives of this study are to determine if daily supportive email messages plus weekly mental health literacy information delivered via email can help improve mental health literacy and reduce the prevalence of moderate to high stress symptoms, burnout, moderate to high anxiety symptoms, moderate to high depression symptoms, and low resilience among school teachers in Zambia. The secondary objectives of this study are to evaluate the baseline prevalence and correlates of moderate to high stress, burnout, moderate to high anxiety, moderate to high depression, and low resilience among school teachers in Zambia. METHODS: This is a quantitative longitudinal and cross-sessional study. Data will be collected at the baseline (the onset of the program), 6 weeks, 3 months, 6 months (the program midpoint), and 12 months (the end point) using web-based surveys. Individual teachers will subscribe by accepting an invitation to do so from the Lusaka Apex Medical University organizational account on the ResilienceNHope web-based application. Data will be analyzed using SPSS version 25 with descriptive and inferential statistics. Outcome measures will be evaluated using standardized rating scales. RESULTS: The Wellness4Teachers email program is expected to improve the participating teachers' mental health literacy and well-being. It is anticipated that the prevalence of stress, burnout, anxiety, depression, and low resilience among teachers in Zambia will be similar to those reported in other jurisdictions. In addition, it is expected that demographic, socioeconomic, and organizational factors, class size, and grade teaching will be associated with burnout and other psychological disorders among teachers, as indicated in the literature. Results are expected 2 years after the program's launch. CONCLUSIONS: The Wellness4Teachers email program will provide essential insight into the prevalence and correlates of psychological problems among teachers in Zambia and the program's impact on subscribers' mental health literacy and well-being. The outcome of this study will help inform policy and decision-making regarding psychological interventions for teachers in Zambia. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44370.

Associations of active and passive smartphone use with measures of youth mental health during the COVID-19 pandemic.

Smartphone use provides a significant amount of screen-time for youth, and there have been growing concerns regarding its impact on their mental health. While time spent in a passive manner on the device is frequently considered deleterious, more active engagement with the phone might be protective for mental health. Recent developments in mobile sensing technology provide a unique opportunity to examine behaviour in a naturalistic manner. The present study sought to investigate, in a sample of 451 individuals (mean age 20.97 years old, 83% female), whether the amount of time spent on the device, an indicator of passive smartphone use, would be associated with worse mental health in youth and whether an active form of smartphone use, namely frequent checking of the device, would be associated with better outcomes. The findings highlight that overall time spent on the smartphone was associated with more pronounced internalizing and externalizing symptoms in youth, while the number of unlocks was associated with fewer internalizing symptoms. For externalizing symptoms, there was also a significant interaction between the two types of smartphone use observed. Using objective measures, our results suggest interventions targeting passive smartphone use may contribute to improving the mental health of youth.

A Systematic and Comparative Review of Behavior Change Strategies in Stress Management Apps: Opportunities for Improvement.

Stress is one of the significant triggers of several physiological and psychological illnesses. Mobile health apps have been used to deliver various stress management interventions and coping strategies over the years. However, little work exists on persuasive strategies employed in stress management apps to promote behavior change. To address this gap, we review 150 stress management apps on both Google Play and Apple's App Store in three stages. First, we deconstruct and compare the persuasive/behavior change strategies operationalized in the apps using the Persuasive Systems Design (PSD) framework and Cialdini's Principles of Persuasion. Our results show that the most frequently employed strategies are personalization, followed by self-monitoring, and trustworthiness, while social support strategies such as competition, cooperation and social comparison are the least employed. Second, we compare our findings within the stress management domain with those from other mental health domains to uncover further insights. Finally, we reflect on our findings and offer eight design recommendations to improve the effectiveness of stress management apps and foster future research.

"I Let Depression and Anxiety Drown Me ": Identifying Factors Associated With Resilience Based on Journaling Using Machine Learning and Thematic Analysis.

Over the years, there has been a global increase in the use of technology to deliver interventions for health and wellness, such as improving people's mental health and resilience. An example of such technology is the Q-Life app which aims to improve people's resilience to stress and adverse life events through various coping mechanisms, including journaling. Using a combination of sentiment analysis and thematic analysis methods, this paper presents the results of analyzing 6023 journal entries from 755 users. We uncover both positive and negative factors that are associated with resilience. First, we apply two lexicon-based and eight machine learning (ML) techniques to classify journal entries into positive or negative sentiment polarity, and then compare the performance of these classifiers to determine the best performing classifier overall. Our results show that Support Vector Machine (SVM) is the best classifier overall, outperforming other ML classifiers and lexicon-based classifiers with a high F1-score of 89.7%. Second, we conduct thematic analysis of negative and positive journal entries to identify themes representing factors associated with resilience either negatively or positively, and to determine various coping mechanisms. Our findings reveal 14 negative themes such as stress, worry, loneliness, lack of motivation, sickness, relationship issues, as well as depression and anxiety. Also, 13 positive themes emerged including self-efficacy, gratitude, socialization, progression, relaxation, and physical activity. Seven (7) coping mechanisms are also identified including time management, quality sleep, and mindfulness. Finally, we reflect on our findings and suggest technological interventions that address the negative factors to promote resilience.

Personality-targeted persuasive gamified systems: exploring the impact of application domain on the effectiveness of behaviour change strategies.

Persuasive gamified systems for health are interventions that promote behaviour change using various persuasive strategies. While research has shown that these strategies are effective at motivating behaviour change, there is little knowledge on whether and how the effectiveness of these strategies vary across multiple domains for people of distinct personality traits. To bridge this gap, we conducted a quantitative study with 568 participants to investigate (a) whether the effectiveness of the persuasive strategies implemented vary within each domain (b) whether the effectiveness of various strategies vary across two distinct domains, (c) how people belonging to different personality traits respond to these strategies, and (d) if people high in a personality trait would be influenced by a persuasive strategy within one domain and not in the other. Our results show that there are significant differences in the effectiveness of various strategies across domains and that people's personality plays a significant role in the perceived persuasiveness of different strategies both within and across distinct domains. The Reward strategy (which involves incentivizing users for achieving specific milestones towards the desired behaviour) and the Competition strategy (which involves allowing users to compete with each other to perform the desired behaviour) were effective for promoting healthy eating but not for smoking cessation for people high in Conscientiousness. We provide design suggestions for developing persuasive gamified interventions for health targeting distinct domains and tailored to individuals depending on their personalities.

COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing.

The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. Twenty (20) positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.

Co-Designing a Mobile App to Improve Mental Health and Well-Being: Focus Group Study.

BACKGROUND: Recent advances in mobile technology have created opportunities to develop mobile apps to aid and assist people in achieving various health and wellness goals. Mental health apps hold significant potential to assist people affected by various mental health issues at any time they may need it, considering the ubiquitous nature of mobile phones. However, there is a need for research to explore and understand end users' perceptions, needs, and concerns with respect to such technologies. OBJECTIVE: The aim of this paper is to explore the opinions, perceptions, preferences, and experiences of people who have experienced some form of mental health issues based on self-diagnosis to inform the design of a next-generation mental health app that would be substantially more engaging and effective than the currently available apps to improve mental health and well-being. METHODS: We conducted six focus group sessions with people who had experienced mental health issues based on self-diagnosis (average age 26.7 years, SD 23.63; 16/32, 50% male; 16/32, 50% female). We asked participants about their experiences with mental health issues and their viewpoints regarding two existing mental health apps (the Happify app and the Self-Help Anxiety Management app). Finally, participants were engaged in a design session where they each sketched a design for their ideal mental health and well-being mobile app. RESULTS: Our findings revealed that participants used strategies to deal with their mental health issues: doing something to distract themselves from their current negative mood, using relaxation exercises and methods to relieve symptoms, interacting with others to share their issues, looking for an external source to solve their problems, and motivating themselves by repeating motivational sentences to support themselves or by following inspirational people. Moreover, regarding the design of mental health apps, participants identified that general design characteristics; personalization of the app, including tracking and feedback, live support, and social community; and providing motivational content and relaxation exercises are the most important features that users want in a mental health app. In contrast, games, relaxation audio, the Google map function, personal assistance to provide suggestions, goal setting, and privacy preservation were surprisingly the least requested features. CONCLUSIONS: Understanding end users' needs and concerns about mental health apps will inform the future design of mental health apps that are useful to and used by many people.

Persuasive Apps for Sustainable Waste Management: A Comparative Systematic Evaluation of Behavior Change Strategies and State-of-the-Art.

With the proliferation of ubiquitous computing and mobile technologies, mobile apps are tailored to support users to perform target behaviors in various domains, including a sustainable future. This article provides a systematic evaluation of mobile apps for sustainable waste management to deconstruct and compare the persuasive strategies employed and their implementations. Specifically, it targeted apps that support various sustainable waste management activities such as personal tracking, recycling, conference management, data collection, food waste management, do-it-yourself (DIY) projects, games, etc. The authors who are persuasive technology researchers retrieved a total of 244 apps from App Store and Google Play, out of which 148 apps were evaluated. Two researchers independently analyzed and coded the apps and a third researcher was involved to resolve any disagreement. They coded the apps based on the persuasive strategies of the persuasive system design framework. Overall, the findings uncover that out of the 148 sustainable waste management apps evaluated, primary task support was the most employed category by 89% (n = 131) apps, followed by system credibility support implemented by 76% (n = 112) apps. The dialogue support was implemented by 71% (n = 105) apps and social support was the least utilized strategy by 34% (n = 51) apps. Specifically, Reduction (n = 97), personalization (n = 90), real-world feel (n = 83), surface credibility (n = 83), reminder (n = 73), and self-monitoring (n = 50) were the most commonly employed persuasive strategies. The findings established that there is a significant association between the number of persuasive strategies employed and the apps' effectiveness as indicated by user ratings of the apps. How the apps are implemented differs depending on the kind of sustainable waste management activities it was developed for. Based on the findings, this paper offers design implications for personalizing sustainable waste management apps to improve their persuasiveness and effectiveness.

Mobile Sensing Apps and Self-management of Mental Health During the COVID-19 Pandemic: Web-Based Survey.

BACKGROUND: During the COVID-19 pandemic, people had to adapt their daily life routines to the currently implemented public health measures, which is likely to have resulted in a lack of in-person social interactions, physical activity, or sleep. Such changes can have a significant impact on mental health. Mobile sensing apps can passively record the daily life routines of people, thus making them aware of maladaptive behavioral adjustments to the pandemic. OBJECTIVE: This study aimed to explore the views of people on mobile sensing apps that passively record behaviors and their potential to increase awareness and helpfulness for self-managing mental health during the pandemic. METHODS: We conducted an anonymous web-based survey including people with and those without mental disorders, asking them to rate the helpfulness of mobile sensing apps for the self-management of mental health during the COVID-19 pandemic. The survey was conducted in May 2020. RESULTS: The majority of participants, particularly those with a mental disorder (n=106/148, 72%), perceived mobile sensing apps as very or extremely helpful for managing their mental health by becoming aware of maladaptive behaviors. The perceived helpfulness of mobile sensing apps was also higher among people who experienced a stronger health impact of the COVID-19 pandemic (ß=.24; 95% CI 0.16-0.33; P<.001), had a better understanding of technology (ß=.17; 95% CI 0.08-0.25; P<.001), and had a higher education (ß=.1; 95% CI 0.02-0.19; P=.02). CONCLUSIONS: Our findings highlight the potential of mobile sensing apps to assist in mental health care during the pandemic.

A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study.

BACKGROUND: Internalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders usually rely on subjective symptom ratings, but internalizing symptoms are frequently underreported, which creates a barrier to the accurate assessment of these symptoms in youth. Therefore, novel assessment tools that use objective data need to be developed to meet the highest standards of reliability, feasibility, scalability, and affordability. Mobile sensing technologies, which unobtrusively record aspects of youth behaviors in their daily lives with the potential to make inferences about their mental health states, offer a possible method of addressing this assessment barrier. OBJECTIVE: This study aims to explore whether passively collected smartphone sensor data can be used to predict internalizing symptoms among youth in Canada. METHODS: In this study, the youth participants (N=122) completed self-report assessments of symptoms of anxiety, depression, and attention-deficit hyperactivity disorder. Next, the participants installed an app, which passively collected data about their mobility, screen time, sleep, and social interactions over 2 weeks. Then, we tested whether these passive sensor data could be used to predict internalizing symptoms among these youth participants. RESULTS: More severe depressive symptoms correlated with more time spent stationary (r=0.293; P=.003), less mobility (r=0.271; P=.006), higher light intensity during the night (r=0.227; P=.02), and fewer outgoing calls (r=-0.244; P=.03). In contrast, more severe anxiety symptoms correlated with less time spent stationary (r=-0.249; P=.01) and greater mobility (r=0.234; P=.02). In addition, youths with higher anxiety scores spent more time on the screen (r=0.203; P=.049). Finally, adding passively collected smartphone sensor data to the prediction models of internalizing symptoms significantly improved their fit. CONCLUSIONS: Passively collected smartphone sensor data provide a useful way to monitor internalizing symptoms among youth. Although the results replicated findings from adult populations, to ensure clinical utility, they still need to be replicated in larger samples of youth. The work also highlights intervention opportunities via mobile technology to reduce the burden of internalizing symptoms early on.

Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach.

BACKGROUND: The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. OBJECTIVE: This paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data. METHODS: We applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes. RESULTS: A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS: We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.

Insights from user reviews to improve mental health apps.

Mental health applications hold great promise as interventions for addressing common mental issues. Although many people with mental health issues use mobile app interventions, their adherence level remains low. Low engagement affects the effectiveness of mobile interventions. However, there is still a dearth of research to explain the reasons for low engagement. User experience and usability are two factors that determine the adoption and usage of apps. Analyzing user reviews of mobile apps for mental health issues reveals user experience and what features users liked and disliked in the apps and hence informs future app design and refinements. This research aims to analyze user reviews of publicly available mental health applications to uncover their strengths, weaknesses, and gaps, hence revealing why users are likely to cease using these applications. We mined reviews of 106 mental health apps retrieved from Apple's App Store and Google Play and employed thematic analysis on 13,549 reviews. The review analysis shows that users placed more emphasis on the user interface and the user-friendliness of the app. Users also appreciated apps that present them with a variety of options, functionalities, and content that they can choose. Again, apps that offer adaptive functionalities that allow users to adapt some app features also received high ratings. In contrast, poor usability emerged as the most common reason for abandoning mental health apps. Other pitfalls include lack of a content variety, lack of personalization, lack of customer service and trust, and security and privacy issues.

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach.

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

Effects of a Personalized Fitness Recommender System Using Gamification and Continuous Player Modeling: System Design and Long-Term Validation Study.

BACKGROUND: Gamification and persuasive games are effective tools to motivate behavior change, particularly to promote daily physical activities. On the one hand, studies have suggested that a one-size-fits-all approach does not work well for persuasive game design. On the other hand, player modeling and recommender systems are increasingly used for personalizing content. However, there are few existing studies on how to build comprehensive player models for personalizing gamified systems, recommending daily physical activities, or the long-term effectiveness of such gamified exercise-promoting systems. OBJECTIVE: This paper aims to introduce a gamified, 24/7 fitness assistant system that provides personalized recommendations and generates gamified content targeted at individual users to bridge the aforementioned gaps. This research aims to investigate how to design gamified physical activity interventions to achieve long-term engagement. METHODS: We proposed a comprehensive model for gamified fitness recommender systems that uses detailed and dynamic player modeling and wearable-based tracking to provide personalized game features and activity recommendations. Data were collected from 40 participants (23 men and 17 women) who participated in a long-term investigation on the effectiveness of our recommender system that gradually establishes and updates an individual player model (for each unique user) over a period of 60 days. RESULTS: Our results showed the feasibility and effectiveness of the proposed system, particularly for generating personalized exercise recommendations using player modeling. There was a statistically significant difference among the 3 groups (full, personalized, and gamified) for overall motivation (F3,36=22.49; P<.001), satisfaction (F3,36=22.12; P<.001), and preference (F3,36=15.0; P<.001), suggesting that both gamification and personalization have positive effects on the levels of motivation, satisfaction, and preference. Furthermore, qualitative results revealed that a customized storyline was the most requested feature, followed by a multiplayer mode, more quality recommendations, a feature for setting and tracking fitness goals, and more location-based features. CONCLUSIONS: On the basis of these results and drawing from the gamer modeling literature, we conclude that personalizing recommendations using player modeling and gamification can improve participants' engagement and motivation toward fitness activities over time.