Higher Engagement Research Articles

Vicarious learning and graduates' adaptive career behaviours: the case of graduates on clinical internships

PurposeThe effect of vicarious learning during clinical or medical internships on graduates' adaptive career behaviours has attracted scant attention from healthcare researchers, particularly, in the developing world context. Drawing upon the social cognitive career theory model of career self-management (SCCT-CSM), the current study examines how vicarious learning influences the clinical graduates' adaptive career behaviours (i.e. career exploration and decision-making) via career exploration and decision-making self-efficacy (CEDSE) and career intention.Design/methodology/approachData were collected from 293 nursing graduates undertaking clinical internships in 25 hospitals across Nigeria who willingly participated in this study as they were also assured of confidentiality at two-waves. The proposed hypotheses were tested using a path analysis.FindingsThe findings showed that vicarious learning during clinical internship had a direct effect on career exploration, decision-making and career decision self-efficacy among graduate trainees. Also, the findings revealed that the effects of vicarious learning on the graduates' career exploration and career decision-making were significantly mediated by career decision self-efficacy and career intentions.Practical implicationsThe findings of this study have important practical implications for higher education institutions and industries that send and receive clinical graduates for clinical internships to gain more skills. More emphasis should be on encouraging learners to learn vicariously in addition to other forms of learning experiences available during clinical internships.Originality/valueThe study explains that the graduates' higher engagement in clinical career exploration and decision-making was based on a higher level of vicarious learning during internships. The results suggest that higher education institutions and healthcare service providers can derive greater benefits from more emphasis on promoting vicarious learning during clinical internships.

Breaking presence in Immersive Virtual Reality toward behavioral and emotional engagement

Background and objectiveMany recent studies in virtual reality (VR) have managed the sense of Presence to assess the suitability of their designs, mainly when focused on learning goals that require high user engagement, such as in serious games for psychomotor training. However, the place and plausibility illusions needed to promote Presence are achieved by combining different VR-based design cues, and their individual contribution to preserving the Presence's engagement/involvement component is still unclear. This article explored the single effect of breaking the sense of Presence per VR factor, i.e., removing VR cues related to Social Presence (human interactions), Self-Presence (embodiment), and Physical Presence (Scenario realism). MethodsThirty-three participants were asked to play an immersive VR simulation of an arcade game three times by experiencing a stepped Break of Illusion in one of the VR factors, i.e., while two factors were kept high, the remaining one was reduced to a low and null (hypothetical) level. The game difficulty was fixed after assessing each person's skills. ResultsResults showed that psychophysiology indicators (heart rate and skin conductance) were not affected by the level of illusion, whereas exercise intensity was significantly higher with low body and social presence-based conditions. Moreover, skin conductance was lower in the Social-presence group, which suggests that perspiration is only affected by breaks in realism (scenario and body representations). Based on the obtained evidence, we proposed some guidelines for adapting the design of immersive virtual environments through Breaks in Presence, mainly by changing the realism of the scenario and body representation depending on the skin conductance or the interaction with virtual humans depending on exercise intensity.

Enhancing User Privacy Through Ephemeral Sharing Design: Experimental Evidence from Online Dating

In the dynamic world of online dating, a key challenge faced by platforms is the cold-start problem, where newly matched users are hesitant to engage due to privacy concerns. Our solution, ephemeral sharing, addresses this by balancing privacy with the need for personal information sharing. This feature allows personal photos to disappear and become untraceable soon after being viewed, reassuring users about their privacy. We conducted a large-scale randomized experiment with more than 70,000 users to evaluate the impact of ephemeral sharing. The results are compelling: users who could share ephemeral photos were more likely to send personal images alongside with their matching request, especially those with human faces, leading to more matches and higher engagement. Significantly, this effect was more pronounced among users who are more sensitive to their privacy. Furthermore, ephemeral sharing was found to reduce users’ concerns related to data collection, dissemination, and identity misuse, thereby increasing the willingness to share personal information. This approach not only enhances user privacy but also stimulates more active engagement on the platform. For dating platforms and similar platforms, adopting ephemeral sharing can revolutionize user experience. It provides a strategic advantage by boosting user personal information sharing and enhancing privacy, crucial for maintaining meaningful communication in online dating. This feature represents a significant step forward in designing user-centric, privacy-conscious platforms.

Social engagement and cognitive impairment among nursing home residents: The role of sensory impairment

Background and objectivesUsing US national nursing home data, this cross-sectional study sought to evaluate 1) the association between lack of social engagement and level of cognitive impairment; and 2) the extent to which this association differs by hearing and visual impairment. Research design and methodsOur sample included 793,846 nursing home residents aged ≥ 50 years. The Index of Social Engagement was categorized as none/lower (0, 1, 2) or higher levels (3 through 6). Cognitive Performance Scale was grouped as intact/mild (0, 1, 2), moderate (3, 4), or severe (5, 6). Multinomial models provided adjusted odds ratio (aOR) and 95 % confidence intervals (CI) between none/lower social engagement and cognitive impairment. We estimated relative excess risk due to interaction (RERI) to quantify the joint effects of social engagement and sensory impairment types. ResultsOverall, 12.6 % had lower social engagement, 30.3 % had hearing impairment, and 40.3 % had visual impairment. Compared to residents with high social engagement, those with lower social engagement were more likely to have moderate/severe cognitive impairment (aORmoderate = 2.21, 95 % CI 2.17–2.26; aORsevere = 6.49, 95 % CI 6.24–6.74). The impact of low social engagement on cognitive impairment was more profound among residents with hearing impairment and/or visual impairment (RERIhearing = 3.89, 95 % CI 3.62–4.17; RERIvisual = 25.2, 95 % CI 23.9–26.6)). Discussion and implicationsResidents with lower social engagement had higher levels of cognitive impairment. Residents with sensory impairments are potentially more susceptible to the negative impact of lower levels of social engagement on level of cognitive impairment.

Cancer-on-a-chip model shows that the adenomatous polyposis coli mutation impairs T cell engagement and killing of cancer spheroids

Evaluating the ability of cytotoxic T lymphocytes (CTLs) to eliminate tumor cells is crucial, for instance, to predict the efficiency of cell therapy in personalized medicine. However, the destruction of a tumor by CTLs involves CTL migration in the extra-tumoral environment, accumulation on the tumor, antigen recognition, and cooperation in killing the cancer cells. Therefore, identifying the limiting steps in this complex process requires spatio-temporal measurements of different cellular events over long periods. Here, we use a cancer-on-a-chip platform to evaluate the impact of adenomatous polyposis coli (APC) mutation on CTL migration and cytotoxicity against 3D tumor spheroids. The APC mutated CTLs are found to have a reduced ability to destroy tumor spheroids compared with control cells, even though APC mutants migrate in the extra-tumoral space and accumulate on the spheroids as efficiently as control cells. Once in contact with the tumor however, mutated CTLs display reduced engagement with the cancer cells, as measured by a metric that distinguishes different modes of CTL migration. Realigning the CTL trajectories around localized killing cascades reveals that all CTLs transition to high engagement in the 2 h preceding the cascades, which confirms that the low engagement is the cause of reduced cytotoxicity. Beyond the study of APC mutations, this platform offers a robust way to compare cytotoxic cell efficiency of even closely related cell types, by relying on a multiscale cytometry approach to disentangle complex interactions and to identify the steps that limit the tumor destruction.

Navigating Risk:: Understanding Chronic Disease Factors in Delaware's College Population.

This study aimed to assess the prevalence of chronic disease risk factors among college students, particularly within a minority-serving institution in Delaware, to inform targeted prevention efforts. A quantitative cross-sectional research design was employed, administering surveys to 457 students at Delaware State University over a three-year period. Surveys assessed tobacco use, physical activity, and dietary habits. Descriptive statistics were used to analyze the data. The majority of respondents were people of color (83%) and female (74%). Significant findings included 24.1% reporting trying tobacco cigarettes, 4.9% smoking in the last 30 days, and 37.2% trying e-cigarettes, with 11.8% vaping in the last month. Regarding physical activity, 65.6% reported being active for 120+ minutes per week, while only 5.7% consumed four or more servings of vegetables daily. Behavioral disparities were observed, with more males reporting tobacco use but also higher engagement in physical activity compared to females. Nutritional intake was inadequate for both genders. Convenience sampling may limit generalizability, highlighting the need for larger, randomized studies. Colleges are pivotal settings for promoting healthy behaviors and addressing chronic disease burdens. Integrated approaches involving education, policy changes, and community engagement are crucial. Investing in health promotion programs is essential for cultivating a healthier future population. This study underscores the importance of preventive measures and equitable access to resources for addressing chronic disease risk factors among college students.

Leveraging Machine Learning to Develop Digital Engagement Phenotypes of Users in a Digital Diabetes Prevention Program: Evaluation Study.

Digital diabetes prevention programs (dDPPs) are effective "digital prescriptions" but have high attrition rates and program noncompletion. To address this, we developed a personalized automatic messaging system (PAMS) that leverages SMS text messaging and data integration into clinical workflows to increase dDPP engagement via enhanced patient-provider communication. Preliminary data showed positive results. However, further investigation is needed to determine how to optimize the tailoring of support technology such as PAMS based on a user's preferences to boost their dDPP engagement. This study evaluates leveraging machine learning (ML) to develop digital engagement phenotypes of dDPP users and assess ML's accuracy in predicting engagement with dDPP activities. This research will be used in a PAMS optimization process to improve PAMS personalization by incorporating engagement prediction and digital phenotyping. This study aims (1) to prove the feasibility of using dDPP user-collected data to build an ML model that predicts engagement and contributes to identifying digital engagement phenotypes, (2) to describe methods for developing ML models with dDPP data sets and present preliminary results, and (3) to present preliminary data on user profiling based on ML model outputs. Using the gradient-boosted forest model, we predicted engagement in 4 dDPP individual activities (physical activity, lessons, social activity, and weigh-ins) and general activity (engagement in any activity) based on previous short- and long-term activity in the app. The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the Brier score metrics determined the performance of the model. Shapley values reflected the feature importance of the models and determined what variables informed user profiling through latent profile analysis. We developed 2 models using weekly and daily DPP data sets (328,821 and 704,242 records, respectively), which yielded predictive accuracies above 90%. Although both models were highly accurate, the daily model better fitted our research plan because it predicted daily changes in individual activities, which was crucial for creating the "digital phenotypes." To better understand the variables contributing to the model predictor, we calculated the Shapley values for both models to identify the features with the highest contribution to model fit; engagement with any activity in the dDPP in the last 7 days had the most predictive power. We profiled users with latent profile analysis after 2 weeks of engagement (Bayesian information criterion=-3222.46) with the dDPP and identified 6 profiles of users, including those with high engagement, minimal engagement, and attrition. Preliminary results demonstrate that applying ML methods with predicting power is an acceptable mechanism to tailor and optimize messaging interventions to support patient engagement and adherence to digital prescriptions. The results enable future optimization of our existing messaging platform and expansion of this methodology to other clinical domains. ClinicalTrials.gov NCT04773834; https://www.clinicaltrials.gov/ct2/show/NCT04773834. RR2-10.2196/26750.

Getting creative: Using art-based techniques to identify how arts organizations enhance young people’s well-being

Mental health concerns present significant challenges for Australian youth. Arts organizations play a key role in promoting preventative mental health strategies through enhancing the social and emotional well-being (SEWB) of youth. However, little is known about how the arts promote SEWB and the processes and contexts through which this occurs. This article describes a 2021 workshop conducted on the lands of Whadyuk Noongar in Boorloo, Perth, Western Australia as phase 1 of a research project aimed to improve the capacity of arts organizations to promote SEWB. Utilizing tableau, lightbox activities, and focus groups, the workshop employed art-based data-collection to identify approaches used by arts organizations to promote the SEWB of youth. The approaches encompassed developing empowerment and agency, creating safe spaces, connecting, and collaborating and encouraging emotion awareness. Given the high engagement of participants in the data collection process, art-based activities will be used in future phases of the project.

Effects of using audience response systems (Kahoot and Google Forms and Sheets) on learning of medical students in a large class: A questionnaire-based retrospective study

AbstractThe effectiveness of implementing various audience response system (ARS) formats in large classes to engage students, positively influence academic outcomes, and align with their preferences for different types of activities lacked sufficient information. This study aimed to (1) evaluate students’ perspectives on different ARS formats, including Kahoot for individual (Kahoot-I) and team (Kahoot-T) play, and Google Forms and Google Sheets for multiple-choice questions (Google-MCQ) and for open-ended questions (Google-OEQ) on different aspects with/without subgroup analysis of students into quartiles (Q1-lowest-Q4-highest) of summative scores, and (2) determine correlations between students’ perspectives and academic outcomes. At the course’s final class, a QR code for a 5-point Likert scale questionnaire was displayed for all enrolled medical students. 269/312 students (86.22%) provided written consent and completed the questionnaire constructed based on dimensions of student engagement. Across all ARS formats, students rated scores ≥ 4 for almost all aspects, with participation in class receiving the highest scores. For each aspect, Kahoot-T was rated highest in various aspects and Google-MCQ for “understanding” and Google-OEQ for “asking/answering question”. Higher academic achievers expressed more “enthusiasm” for Kahoot-I than lower achievers (P < 0.05). Formative and summative scores were positively correlated with many aspects. Students reported high engagement with all ARS formats, with higher academic achievers rating them with higher scores. Kahoot-T was the most favored for multiple aspects, Google-MCQ for “understanding” and Google-OEQ for interactive “asking/answering question”. Thus, selecting ARS in alignment with learning objective could enhance students’ engagement and learning outcomes.

A Comprehensive Analysis of Influencer Types in Digital Marketing

This study conducts a comprehensive qualitative analysis of influencer marketing, focusing on Mega, Macro, Micro, Nano, Blog, Vlogging, and Gaming influencers. Each category, distinguished by audience size and engagement strategies, plays a unique role in digital marketing. Mega influencers offer broad brand awareness but require high engagement, while macro-influencers balance broad appeal with niche authority. Micro-influencers excel in niche market trust-building, Nano influencers offer authentic localized potential, and Blog influencers provide SEO-rich content for sustained visibility. Vlogging influencers use dynamic video storytelling, and Gaming influencers impact tech communities. The study emphasizes aligning influencer types with specific goals and target audiences for effective campaigns, providing marketers with a strategic framework for navigating the diverse influencer ecosystem. The study contributes insights into influencer marketing dynamics, aiding marketers in informed decision-making amid the evolving digital landscape.

Exploring the Use of Cluster Analysis in Market Segmentation for Targeted Advertising

In the rapidly evolving field of digital marketing, precise targeting and segmentation have become essential for optimizing advertising efforts, yet traditional methods often struggle to adequately address the dynamic nature of consumer behaviors. This study delves into the efficacy of cluster analysis as a robust tool in market segmentation, particularly aimed at enhancing the precision of targeted advertising campaigns. By employing a case study approach, the research meticulously analyzes real-world advertising campaigns across various industries, utilizing cluster analysis to segment the market and employing qualitative data analysis to evaluate the outcomes in terms of engagement and conversion improvements. The results indicate that cluster analysis not only facilitates a deeper understanding of market segments but also leads to more tailored and effective advertising strategies. Companies implementing this method reported significant improvements in campaign performance, with higher engagement and conversion rates compared to traditional segmentation approaches. This study underscores the advantages of employing advanced statistical methods like cluster analysis in market segmentation, highlighting its potential to transform targeted advertising by enabling advertisers to adapt more swiftly and effectively to market dynamics. The implications for practice suggest that businesses should integrate cluster analysis into their marketing strategies to gain a competitive edge through enhanced customer insights and optimized advertising effectiveness, thereby contributing valuable empirical evidence to the existing literature.

The Effect of Organizational Culture, Work-Life Balance, and Job Satisfaction on Non-Commercial Employee Work Engagement

A person's attachment to their work (work engagement) is a view that is satisfying and provides positive motivation related to work results, which is characterized by a high level of enthusiasm and strong identification with one's work. Employees with high work engagement will be able to mobilize all their abilities for the progress and sustainability of the company. The problem formulation in this research is to determine the influence of Organizational Empowerment, Work Life Balance, and Job Satisfaction on the Work Engagement of Employees of the Non-Commercial Division of PT. Royalboard Banguninti Granito. Research data was taken using observation techniques and questionnaires using Google Form media, with a sample of 56 staff-up employees. The data analysis technique used is multiple linear regression analysis to prove the research hypothesis. Data that has met validity, reliability, and classical assumption tests is then processed to obtain a regression equation. Based on the results of this research, it can be concluded that there is a positive and significant influence of the variables Organizational Culture, Work Life Balance, and Job Satisfaction on Work Engagement among employees PT. Royalboard Banguninti Granito both simultaneously and partially.

Implementation of content and language integrated learning in biology education programs

Relevance. Modern higher education emphasizes not only professional training but also the integration of students into the global professional community through foreign language proficiency, which enhances skills and professional competence. Purpose. This research aims to assess the effectiveness of Content and Language Integrated Learning (CLIL) methodology in biological education programmes at higher education institutions in Kazakhstan. Methodology. A semi-formalized questionnaire survey was conducted across six educational institutions in Kazakhstan, comprising "closed" questions including open-ended list questions, single-choice questions using the Likert scale, and multiple-choice questions, totalling 16 questions. Results. The survey conducted across six educational institutions in Kazakhstan revealed that 85.5% of students perceive Content and Language Integrated Learning (CLIL) positively contributes to their English language acquisition within biological education programmes. This underscores CLIL's effectiveness in enhancing language skills alongside disciplinary knowledge. Despite varying English proficiency levels among participants, the study indicated strong student engagement with CLIL, demonstrating significant interest and active participation in the learning process. Conclusions. The study confirms CLIL's efficacy in enhancing English acquisition within Kazakhstan's biological education. With 85.5% of students noting its positive impact, CLIL shows promise as a key method for integrating language and disciplinary knowledge. Despite varying English levels, high student engagement underscores CLIL's effectiveness. Future steps should focus on formalizing CLIL across Kazakhstan, providing support for educators, and refining methodologies through ongoing research. Implementing CLIL prepares students for global contexts, enhancing educational outcomes and international competitiveness. Keywords: English; pedagogy; teaching methodology; linguistics

Social Interoception and Autonomic System Reactivity during Synchronization Behavior.

Within the social interoception field, little is known about the impact of interoception on autonomic system reactivity during synchronization tasks. The impact of social framing manipulation and Interoceptive Attentiveness (IA; defined as concentrated attention on the breath for a specific time interval) on autonomic responses during interpersonal synchronization was investigated in this research. Under two experimental interoceptive conditions-the concentration and no focus on the breath condition-participants completed two synchronization tasks. A social framing was given to participants by informing them that they needed to complete the tasks in unison to improve their collaboration abilities. Autonomic responses (electrodermal activity and cardiovascular indices) were collected throughout task performance. Two orders of results were observed: high cognitive engagement was detected during the focus on the breath condition and for the social frame. This effect was specifically observed for the motor compared to the linguistic synchronization task. Meanwhile, a potential lack of emotional control was observed in the no focus on the breath condition when the synchronization tasks were not socially framed. Such results encourage the use of the hyperscanning paradigm to deepen the impact of IA in real-time and ecological interpersonal synchronization dynamics.

Exploring the informal learning of zero waste lifestyle in Malaysia with big data analytics

Malaysia's solid waste generation is showing a worrying increasing trend, while public awareness towards achieving zero waste remains low. As a big data resource, social media gives valuable insight into the public's perspective on zero waste and, as such, may be fully utilized as an active informal learning platform to lessen the reliance on formal environmental education. Using big data analytics on Instagram, this study aimed to assess the public knowledge, attitude and practice concerning zero waste lifestyle in Malaysia in order to develop an informal learning strategy on social media. Purposive data sampling was conducted on Phantombuster using zero waste-related Instagram hashtags, which yielded 1723 high engagement posts and 1500 comments from 35 identified hashtags. The recorded posts were analyzed using descriptive statistics and sentiment analysis on Python's TextBlob. A total of 94.3% of posts were published by public and private sector accounts, highlighting their vital role in facilitating active knowledge sharing across the online zero waste communities. The sentiment analysis results indicated 41.3% of comments were fairly positive, while 36.1% were more objective and knowledge oriented, acknowledging the collective individual actions that have initiated influential social change in Malaysia. This study advances the existing literature on zero waste and informal learning by recommending the use of big data analytics on social media in the local context. Only with full commitment from all parties to raising public awareness about waste management will the zero waste nation be realized.

User Engagement, Acceptability, and Clinical Markers in a Digital Health Program for Nonalcoholic Fatty Liver Disease: Prospective, Single-Arm Feasibility Study.

Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in the world. Common comorbidities are central obesity, type 2 diabetes mellitus, dyslipidemia, and metabolic syndrome. Cardiovascular disease is the most common cause of death among people with NAFLD, and lifestyle changes can improve health outcomes. This study aims to explore the acceptability of a digital health program in terms of engagement, retention, and user satisfaction in addition to exploring changes in clinical outcomes, such as weight, cardiometabolic risk factors, and health-related quality of life. We conducted a prospective, open-label, single-arm, 12-week study including 38 individuals with either a BMI >30, metabolic syndrome, or type 2 diabetes mellitus and NAFLD screened by FibroScan. An NAFLD-specific digital health program focused on disease education, lowering carbohydrates in the diet, food logging, increasing activity level, reducing stress, and healthy lifestyle coaching was offered to participants. The coach provided weekly feedback on food logs and other in-app activities and opportunities for participants to ask questions. The coaching was active throughout the 12-week intervention period. The primary outcome was feasibility and acceptability of the 12-week program, assessed through patient engagement, retention, and satisfaction with the program. Secondary outcomes included changes in weight, liver fat, body composition, and other cardiometabolic clinical parameters at baseline and 12 weeks. In total, 38 individuals were included in the study (median age 59.5, IQR 46.3-68.8 years; n=23, 61% female). Overall, 34 (89%) participants completed the program and 29 (76%) were active during the 12-week program period. The median satisfaction score was 6.3 (IQR 5.8-6.7) of 7. Mean weight loss was 3.5 (SD 3.7) kg (P<.001) or 3.2% (SD 3.4%), with a 2.2 (SD 2.7) kg reduction in fat mass (P<.001). Relative liver fat reduction was 19.4% (SD 23.9%). Systolic blood pressure was reduced by 6.0 (SD 13.5) mmHg (P=.009). The median reduction was 0.14 (IQR 0-0.47) mmol/L for triglyceride levels (P=.003), 3.2 (IQR 0.0-5.4) µU/ml for serum insulin (s-insulin) levels (P=.003), and 0.5 (IQR -0.7 to 3.8) mmol/mol for hemoglobin A1c (HbA1c) levels (P=.03). Participants who were highly engaged (ie, who used the app at least 5 days per week) had greater weight loss and liver fat reduction. The 12-week-long digital health program was feasible for individuals with NAFLD, receiving high user engagement, retention, and satisfaction. Improved liver-specific and cardiometabolic health was observed, and more engaged participants showed greater improvements. This digital health program could provide a new tool to improve health outcomes in people with NAFLD. Clinicaltrials.gov NCT05426382; https://clinicaltrials.gov/study/NCT05426382.

Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.

Instagram is a popular social networking platform for sharing photos with a large proportion of youth and young adult users. We aim to identify key features in anti-vaping Instagram image posts associated with high social media user engagement by artificial intelligence. We collected 8972 anti-vaping Instagram image posts and hand-coded 2200 Instagram images to identify nine image features such as warning signs and person-shown vaping. We utilized a deep-learning model, the OpenAI: contrastive language-image pre-training with ViT-B/32 as the backbone and a 5-fold cross-validation model evaluation, to extract similar features from the Instagram image and further trained logistic regression models for multilabel classification. Latent Dirichlet Allocation model and Valence Aware Dictionary and sEntiment Reasoner were used to extract the topics and sentiment from the captions. Negative binomial regression models were applied to identify features associated with the likes and comments count of posts. Several features identified in anti-vaping Instagram image posts were significantly associated with high social media user engagement (likes or comments), such as educational warnings and warning signs. Instagram posts with captions about health risks associated with vaping received significantly more likes or comments than those about help quitting smoking or vaping. Compared to the model based on 2200 hand-coded Instagram image posts, more significant features have been identified from 8972 AI-labeled Instagram image posts. Features identified from anti-vaping Instagram image posts will provide a potentially effective way to communicate with the public about the health effects of e-cigarette use. Considering the increasing popularity of social media and the current vaping epidemic, especially among youth and young adults, it becomes necessary to understand e-cigarette-related content on social media. Although pro-vaping messages dominate social media, anti-vaping messages are limited and often have low user engagement. Using advanced deep-learning and statistical models, we identified several features in anti-vaping Instagram image posts significantly associated with high user engagement. Our findings provide a potential approach to effectively communicate with the public about the health risks of vaping to protect public health.

Barriers to and facilitators of user engagement with web-based mental health interventions in young people: a systematic review.

Many young people (YP) are diagnosed with mental illnesses and require support. Web-based mental health interventions (W-MHIs) have been increasingly utilized by YP, healthcare providers, and parents due to reasons including convenience and anonymity. W-MHIs are effective in improving mental health in YP. However, real-world engagement with W-MHIs remains low. Therefore, understanding barriers/facilitators of user engagement with W-MHIs is necessary to promote W-MHIs and help users gain optimal benefits through higher engagement. This review aims to identify barriers/facilitators of user engagement with W-MHIs in YP aged 10-24years. A systematic search of five databases for English language, peer-reviewed publications was conducted between January 2010 and February 2023. Studies examining factors influencing user engagement with W-MHIs, described as barriers or facilitators, were included. Study quality was assessed using the Mixed Methods Appraisal Tool. A narrative synthesis was performed. Of 4088 articles identified, 69 studies were included. Barriers/facilitators were reported by young people (63 studies), providers (17 studies), and parents/caregivers (8 studies). YP perceived that usefulness and connectedness were the most common facilitators, whereas low-perceived need was the most reported barrier. Both providers and parents reported that perceived usefulness for YP was the most common facilitator, whereas concerns about program effectiveness and privacy were noted as barriers. This review found that program- and individual-related factors were important determinants of engagement with W-MHIs. This review provides guidance on the future design and development of new interventions, narrowing the gap between existing W-MHIs and unmet needs of users.

Communication, understanding and engagement of patients with pancreatic cancer at time of diagnosis

BackgroundObjectives: To investigate communication clarity and understanding at the time of pancreatic adenocarcinoma (PDAC) diagnosis and whether they can influence patient engagement and compliance. MethodsConsecutive PDAC patients were enrolled at the time of diagnosis after obtaining informed consent in a single-center study. The patients completed a validated scale (PHE-s®), and the understanding rate was assessed using standardized tools. Patient compliance was evaluated, and the correlation between the PHE-s®, understanding, and compliance was calculated. ResultsThirty patients were enrolled (15 female) with a mean age 64.4, 13 were metastatic. The mean visit time was 31 min, being longer if visiting doctor was an oncologist (p = 0.002). The engagement level was high in 70% of the patients, and all but one were compliant. The analysis of doctor-patient interactions showed a median of 121 conversational turns for doctors, 75 for patients, and 20 for caregivers (p < 0.0001), and the median percentage of speaking time was 77% for doctors, 13% for patients, and 2% for caregivers (p < 0.0001). Female caregivers spent more time speaking than did male caregivers (median 11.6% vs. 1.3%; p = 0.06). There were 290 instances of problematic understanding, most of which occurred during the taking of patients' personal medical history for doctors, while for patients and caregivers, these occurred mainly during the discussion of diagnosis/treatment (p < 0.0001). In a multivariable analysis, only origin from central or southern Italy was associated with high engagement (p = 0.0087). ConclusionIn this first attempt to measure clarity of communication and engagement in patients with PDAC, typical features of conversation and problematic understanding emerged, which deserves further investigation.

A Self-Help Crisis Outreach Effort for At-Risk Primary Care Patients: A Pilot Study of Veterans During the COVID-19 Pandemic.

Stressful events can exacerbate symptoms of psychiatric disorders among primary care patients, putting them at increased risk for suicide. In a pilot study that ran from August to December of 2020, researchers evaluated the acceptability and implementation of Managing Emotions in Disaster and Crisis (MEDIC), a self-help intervention designed to assist at-risk primary care patients. A total of 108 at-risk veterans completed baseline and 6-week assessments. Results were promising, with high patient acceptability and engagement along with improvement in all measures of mental illness symptoms from baseline to posttreatment. Self-help interventions like MEDIC may offer a low-burden way for primary care providers to support more patients.